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Model: chanifrusydi/gemma-3-270m-lora-finetuned
Source: Original Platform

.gitattributes

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gguf/gemma-3-270m-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+gguf/gemma-3-270m-BF16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: unsloth/gemma-3-270m-it
+library_name: transformers
+pipeline_tag: text-generation
+license: gemma
+tags:
+- gemma3
+- lora
+- sft
+- unsloth
+- gguf
+---
+
+# gemma-3-270m-lora-finetuned
+
+A fine-tuned Gemma 3 270M model with LoRA adapters, trained on instruction-following data.
+
+## Model Details
+
+- **Base Model:** unsloth/gemma-3-270m-it
+- **Model Type:** LoRA fine-tuned (merged)
+- **Language:** English (instruction-tuned)
+- **License:** Gemma License
+
+## Available Formats
+
+| Format | File | Use Case |
+|--------|------|----------|
+| Safetensors | `model.safetensors` | Transformers, vLLM |
+| GGUF Q8_0 | `gguf/gemma-3-270m-Q8_0.gguf` | llama.cpp, Ollama |
+| GGUF BF16 | `gguf/gemma-3-270m-auto.gguf` | llama.cpp (full precision) |
+
+## Uses
+
+### Direct Use
+- Instruction following
+- Conversational AI
+- Question answering
+- Text generation
+
+### Out-of-Scope Use
+- Medical or legal advice
+- Generating harmful content
+- Production use without evaluation
+
+## Training Details
+
+### Training Data
+- **Dataset:** mlabonne/FineTome-100k (full dataset)
+- **Samples:** ~100,000
+- **Data Processing:** Standardized format with Gemma 3 chat template
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Precision | bfloat16 |
+| Max Sequence Length | 1024 |
+| Per Device Batch Size | 2 |
+| Gradient Accumulation Steps | 4 |
+| Effective Batch Size | 8 |
+| Max Steps | 12,500 |
+| Learning Rate | 2e-4 |
+| Warmup Steps | 2 |
+| Optimizer | adamw_8bit |
+| Weight Decay | 0.001 |
+| LR Scheduler | linear |
+| Seed | 3407 |
+
+### LoRA Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank (r) | 128 |
+| LoRA Alpha | 128 |
+| LoRA Dropout | 0 |
+| Bias | none |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+
+### Training Stats
+- **Training Time:** ~98 minutes (1h 38m)
+- **Peak VRAM Usage:** 12.4 GB (77.9% of 16 GB)
+
+### Compute Infrastructure
+- **Hardware:** NVIDIA RTX 5070 Ti (16 GB VRAM)
+- **Framework:** Unsloth, Transformers, TRL
+
+## How to Get Started
+
+### Using Transformers (Safetensors)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
+
+messages = [{"role": "user", "content": "Hello, how are you?"}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+
+output = model.generate(inputs, max_new_tokens=64, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Using GGUF (llama.cpp / Ollama)
+```bash
+# Download GGUF
+wget https://huggingface.co/chanifrusydi/gemma-3-270m-lora-finetuned/resolve/main/gguf/gemma-3-270m-Q8_0.gguf
+
+# Run with llama.cpp
+./llama-cli -m gemma-3-270m-Q8_0.gguf -p "Hello, how are you?"
+
+# Or import to Ollama
+ollama create gemma3-finetuned -f Modelfile
+```
+
+## Citation
+
+```bibtex
+@misc{gemma-3-270m-lora-finetuned,
+  author = {chanifrusydi},
+  title = {gemma-3-270m-lora-finetuned},
+  publisher = {HuggingFace},
+  year = {2025}
+}
+```
+
+## Acknowledgments
+
+- [Unsloth](https://github.com/unslothai/unsloth) for the fast fine-tuning framework
+- [Google](https://huggingface.co/google) for the Gemma 3 base model
+- [mlabonne](https://huggingface.co/mlabonne) for the FineTome-100k dataset

added_tokens.json

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

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{ '<start_of_turn>model
+' }}
+{%- endif -%}

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,
+  "dtype": "bfloat16",
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "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"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 512,
+  "transformers_version": "4.57.5",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.1.2",
+  "use_bidirectional_attention": false,
+  "use_cache": true,
+  "vocab_size": 262144
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "max_length": 32768,
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.57.5"
+}

gguf/gemma-3-270m-BF16.gguf

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model.safetensors

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special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<end_of_turn>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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