model card update

README.md

@@ -6,10 +6,10 @@ tags:
 - granite-4.0
 ---
 
-# Granite-4.0-H-Micro
+# Granite-4.0-Micro
 
 **Model Summary:**
-Granite-4.0-H-Micro is a 3B parameter long-context instruct model finetuned from *Granite-4.0-H-Micro-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications.
+Granite-4.0-Micro is a 3B parameter long-context instruct model finetuned from *Granite-4.0-Micro-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications.
 
 - **Developers:** Granite Team, IBM
 - **HF Collection:** [Granite 4.0 Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-40-language-models-6811a18b820ef362d9e5a82c)
@@ -22,7 +22,7 @@ Granite-4.0-H-Micro is a 3B parameter long-context instruct model finetuned from
 English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these languages.
 
 **Intended use:** 
-The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
+The model is designed to follow general instructions and can serve as the foundation for AI assistants across diverse domains, including business applications, as well as for LLM agents equipped with tool-use capabilities.
 
 *Capabilities*
 * Summarization
@@ -38,7 +38,7 @@ The model is designed to respond to general instructions and can be used to buil
  -->
  
 **Generation:** 
-This is a simple example of how to use Granite-4.0-H-Micro model.
+This is a simple example of how to use Granite-4.0-Micro model.
 
 Install the following libraries:
 
@@ -54,7 +54,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 device = "cuda"
-model_path = "ibm-granite/granite-4.0-h-micro"
+model_path = "ibm-granite/granite-4.0-micro"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 # drop device_map if running on CPU
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
@@ -82,11 +82,21 @@ Expected output:
 ```
 
 **Tool-calling:** 
-Granite-4.0-H-Micro comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of  tools please follow OpenAI's function [definition schema](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#defining-functions). 
+Granite-4.0-Micro comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of  tools please follow OpenAI's function [definition schema](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#defining-functions). 
 
-This is an example of how to use Granite-4.0-H-Micro model tool-calling ability:
+This is an example of how to use Granite-4.0-Micro model tool-calling ability:
 
 ```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"
+model_path = "ibm-granite/granite-4.0-micro"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# drop device_map if running on CPU
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
+model.eval()
+
 tools = [
     {
         "type": "function",
@@ -170,7 +180,7 @@ For each tool call, return a json object with function name and arguments within
   </tr></thead>
   <tbody>
   <tr>
-    <td style="text-align:left; background-color: #DAE8FF; color: #2D2D2D;">Granite-4.0-H-Micro</td>
+    <td style="text-align:left; background-color: #DAE8FF; color: #2D2D2D;">Granite-4.0-Micro</td>
     <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;"></td>
     <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;"></td>
     <td style="text-align:center; background-color: #DAE8FF; color: #2D2D2D;"></td>
@@ -243,7 +253,7 @@ For each tool call, return a json object with function name and arguments within
   </tr></thead>
   <tbody>
   <tr>
-    <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-4.0-H-Micro</td>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-4.0-Micro</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
@@ -285,7 +295,8 @@ For each tool call, return a json object with function name and arguments within
 </tbody></table> -->
 
 **Model Architecture:** 
-Granite-4.0-H-Micro baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, Mamba2, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
+
+Granite-4.0-Micro baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
 
 <table>
 <thead>
@@ -299,58 +310,58 @@ Granite-4.0-H-Micro baseline is built on a decoder-only dense transformer archit
 <tbody>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Embedding size</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">2560</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">2048</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">2560</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">2048</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">1536</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">4096</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of layers</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">40 attention</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">4 attention / 36 Mamba2</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">40 attention</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">4 attention / 36 Mamba2</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">4 attention / 36 Mamba2</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">4 attention / 36 Mamba2</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Attention head size</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of attention heads</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">40</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">32</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">40</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">12</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of KV heads</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">8</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">4</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Mamba2 state size</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">128</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of Mamba2 heads</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">48</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
   </tr>
 
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP / Shared expert hidden size</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">8192</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">8192</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">8192</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">1024</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">1536</td>
   </tr>
@@ -358,59 +369,59 @@ Granite-4.0-H-Micro baseline is built on a decoder-only dense transformer archit
 
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Num. Experts</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">72</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Num. active Experts</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">6</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">10</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Expert hidden size</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">512</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">768</td>
   </tr>
 
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP activation</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">SwiGLU</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
   </tr>
 
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Sequence length</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">128K</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;">Position embedding</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
-    <td style="text-align:center; background-color: #DAE8FF; color: black;">NoPE</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">RoPE</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">NoPE</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">NoPE</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">NoPE</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;"># Parameters</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">3B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">7B</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">32B</td>
   </tr>
   <tr>
     <td style="text-align:left; background-color: #FFFFFF; color: black;"># Active parameters</td>
-    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
     <td style="text-align:center; background-color: #DAE8FF; color: black;">3B</td>
+    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">2B</td>
     <td style="text-align:center; background-color: #FFFFFF; color: black;">9B</td>
   </tr>

config.json

@@ -9,76 +9,76 @@
   "embedding_multiplier": 12,
   "eos_token_id": 100257,
   "hidden_act": "silu",
-  "hidden_size": 2048,
+  "hidden_size": 2560,
   "initializer_range": 0.1,
   "intermediate_size": 8192,
   "layer_types": [
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
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-    "mamba",
-    "mamba",
-    "mamba",
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-    "mamba",
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-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
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-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba",
-    "attention",
-    "mamba",
-    "mamba",
-    "mamba",
-    "mamba"
+    "attention",
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+    "attention",
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   ],
-  "logits_scaling": 8,
+  "logits_scaling": 10,
   "mamba_chunk_size": 256,
   "mamba_conv_bias": true,
   "mamba_d_conv": 4,
-  "mamba_d_head": 64,
-  "mamba_d_state": 128,
+  "mamba_d_head": 40,
+  "mamba_d_state": 256,
   "mamba_expand": 2,
   "mamba_n_groups": 1,
-  "mamba_n_heads": 64,
+  "mamba_n_heads": 128,
   "mamba_proj_bias": false,
   "max_position_embeddings": 131072,
   "model_type": "granitemoehybrid",
   "normalization_function": "rmsnorm",
-  "num_attention_heads": 32,
+  "num_attention_heads": 40,
   "num_experts_per_tok": 0,
   "num_hidden_layers": 40,
   "num_key_value_heads": 8,
   "num_local_experts": 0,
   "output_router_logits": false,
   "pad_token_id": 100256,
-  "position_embedding_type": "nope",
+  "position_embedding_type": "rope",
   "residual_multiplier": 0.22,
   "rms_norm_eps": 1e-05,
   "rope_scaling": null,
-  "rope_theta": 10000,
+  "rope_theta": 10000000,
   "router_aux_loss_coef": 0.01,
   "shared_intermediate_size": 8192,
   "tie_word_embeddings": true,

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"timestampVerificationData": {}}, "dsseEnvelope": {"payload": 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"payloadType": "application/vnd.in-toto+json", "signatures": [{"sig": "MEQCIB20gwgh71feTrUENH84QDK3y7nmsPoiILXZTjOVC2hCAiA5SwTFrCon2Fd89GXT5q+YUQy4Km8r8TdWzN2munA4AQ=="}]}}