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Model: lunahr/gemma-3-4b-abliterated
Source: Original Platform

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README.md

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+---
+base_model:
+- gghfez/gemma-3-4b-novision
+license: gemma
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Gemma 3 4B (abliterated text-only) model card
+This is an abliterated text-only version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it), created using Baukit.
+
+The vision encoders were removed by [gghf](https://huggingface.co/gghfez). Please note that this model may exhibit a reduced performance.
+
+## Model Description
+
+- **Original Model**: The original Gemma-3-4b-it is a multimodal model released by Google that can process both text and images
+- **This Version**: This version has been modified to use the same architecture as the text-only 1b model, with the vision components removed
+- **Parameters**: 4 billion parameters
+- **Conversion Process**: Vision-related components were stripped while maintaining the text generation capabilities
+
+## Usage
+
+You can load and use this model the same way you would use the text-only [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) version:
+
+```python
+from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
+import torch
+
+model_id = "gghfez/gemma-3-4b-novision"
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+model = Gemma3ForCausalLM.from_pretrained(
+    model_id, quantization_config=quantization_config
+).eval()
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "You are a helpful assistant."},]
+        },
+        {
+            "role": "user",
+            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
+        },
+    ],
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device).to(torch.bfloat16)
+
+
+with torch.inference_mode():
+    outputs = model.generate(**inputs, max_new_tokens=64)
+
+outputs = tokenizer.batch_decode(outputs)
+```
+

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

config.json

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+{
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10240,
+  "max_position_embeddings": 131072,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 34,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": {
+    "factor": 8.0,
+    "rope_type": "linear"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 1024,
+  "sliding_window_pattern": 6,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0.dev0",
+  "use_cache": true,
+  "vocab_size": 262208
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.50.0.dev0"
+}

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