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Model: SindreLinden/gemma-3-1b-ifs-cloud-gguf
Source: Original Platform

.gitattributes

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+gemma-3-1b-ifs-cloud-gguf-F16.gguf filter=lfs diff=lfs merge=lfs -text
+gemma-3-1b-ifs-cloud-gguf-BF16.gguf filter=lfs diff=lfs merge=lfs -text
+gemma-3-1b-ifs-cloud-gguf-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: gemma
+base_model: google/gemma-3-1b-it
+tags:
+- fine-tuned
+- gguf
+- gemma
+- instruct
+- 1b
+model_type: gemma3
+quantized: f16
+datasets:
+- custom
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Fine-tuned Gemma 3 1B IT (GGUF)
+
+This is a fine-tuned version of Google's Gemma 3 1B IT model, converted to GGUF format for efficient inference.
+
+## Model Details
+
+- **Base Model**: google/gemma-3-1b-it
+- **Fine-tuning Method**: QLoRA (Quantized Low-Rank Adaptation)
+- **Format**: GGUF (F16 precision)
+- **Size**: ~1.9GB
+
+## Training Details
+
+- **Adapter**: QLoRA with rank 32, alpha 16
+- **Target Modules**: up_proj, down_proj, gate_proj, q_proj, k_proj, v_proj, o_proj
+- **Sequence Length**: 2048
+- **Training Data**: Custom dataset with sample packing
+- **Epochs**: 3
+- **Learning Rate**: 0.0004 with cosine scheduler
+- **Optimizer**: AdamW BNB 8-bit
+
+## Usage
+
+This GGUF model can be used with various inference engines:
+
+### llama.cpp
+```bash
+./llama-server -m model.gguf
+```
+
+### Ollama
+```bash
+# Create a Modelfile
+FROM model.gguf
+TEMPLATE """<start_of_turn>user
+{{ .Prompt }}<end_of_turn>
+<start_of_turn>model
+"""
+
+# Import the model
+ollama create my-gemma-model -f Modelfile
+ollama run my-gemma-model
+```
+
+### Python with llama-cpp-python
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="model.gguf")
+output = llm("Tell me about artificial intelligence", max_tokens=512)
+print(output)
+```
+
+## Chat Template
+
+This model uses Gemma 3's chat template:
+```
+<start_of_turn>user
+{user_message}<end_of_turn>
+<start_of_turn>model
+{assistant_response}<end_of_turn>
+```
+
+## Limitations
+
+- This is a small 1B parameter model with inherent limitations
+- Fine-tuned for specific use cases - performance may vary on other tasks
+- GGUF conversion may introduce minor numerical differences compared to the original model
+
+## License
+
+This model inherits the license from the base Gemma model. Please refer to Google's Gemma license for usage terms.

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": 512,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": true,
+  "vocab_size": 262145
+}

gemma-3-1b-ifs-cloud-gguf-BF16.gguf

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gemma-3-1b-ifs-cloud-gguf-F16.gguf

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gemma-3-1b-ifs-cloud-gguf-Q8_0.gguf

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