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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: google/gemma-3-1b-it
+library_name: peft
+pipeline_tag: text-generation
+language: en
+license: mit
+tags:
+- lora
+- sft
+- transformers
+- trl
+- unsloth
+- fine-tuned
+datasets:
+- Vezora/Tested-22k-Python-Alpaca
+---
+# Gemma3-Python-22k-1B
+
+A fine-tuned Gemma 3 1B model, fine tuned on the Vezora/Tested-22k-Python-Alpaca dataset. 
+
+## Model Details
+
+This model is a fine-tuned version of google/gemma-3-1b-it using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.
+
+- **Developed by:** theprint
+- **Model type:** Causal Language Model (Fine-tuned with LoRA)
+- **Language:** en
+- **License:** mit
+- **Base model:** google/gemma-3-1b-it
+- **Fine-tuning method:** LoRA with rank 128
+
+## Intended Use
+
+Light coding support (Python only), project feedback and brainstorming.
+
+
+## GGUF Quantized Versions
+
+Quantized GGUF versions are available in the [theprint/Gemma3-Python-22k-1B-GGUF](https://huggingface.co/theprint/Gemma3-Python-22k-1B-GGUF) repo.
+
+- `Gemma3-Python-22k-1B-f16.gguf` (2489.6 MB) - 16-bit float (original precision, largest file)
+- `Gemma3-Python-22k-1B-q3_k_m.gguf` (850.9 MB) - 3-bit quantization (medium quality)
+- `Gemma3-Python-22k-1B-q4_k_m.gguf` (966.7 MB) - 4-bit quantization (medium, recommended for most use cases)
+- `Gemma3-Python-22k-1B-q5_k_m.gguf` (1027.9 MB) - 5-bit quantization (medium, good quality)
+- `Gemma3-Python-22k-1B-q6_k.gguf` (1270.9 MB) - 6-bit quantization (high quality)
+- `Gemma3-Python-22k-1B-q8_0.gguf` (1325.8 MB) - 8-bit quantization (very high quality)
+
+## Training Details
+
+### Training Data
+
+The data set used is [Vezora/Tested-22k-Python-Alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca).
+
+- **Dataset:** Vezora/Tested-22k-Python-Alpaca
+- **Format:** alpaca
+
+### Training Procedure
+
+- **Training epochs:** 2
+- **LoRA rank:** 128
+- **Learning rate:** 3e-05
+- **Batch size:** 4
+- **Framework:** Unsloth + transformers + PEFT
+- **Hardware:** NVIDIA RTX 5090
+
+## Usage
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="theprint/Gemma3-Python-22k-1B",
+    max_seq_length=4096,
+    dtype=None,
+    load_in_4bit=True,
+)
+
+# Enable inference mode
+FastLanguageModel.for_inference(model)
+
+# Example usage
+inputs = tokenizer(["Your prompt here"], return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Alternative Usage (Standard Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "theprint/Gemma3-Python-22k-1B",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("theprint/Gemma3-Python-22k-1B")
+
+# Example usage
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Your question here"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Using with llama.cpp
+
+```bash
+# Download a quantized version (q4_k_m recommended for most use cases)
+wget https://huggingface.co/theprint/Gemma3-Python-22k-1B/resolve/main/gguf/Gemma3-Python-22k-1B-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama.cpp/main -m Gemma3-Python-22k-1B-q4_k_m.gguf -p "Your prompt here" -n 256
+```
+## Limitations
+
+May provide incorrect information.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{gemma3_python_22k_1b,
+  title={Gemma3-Python-22k-1B: Fine-tuned google/gemma-3-1b-it},
+  author={theprint},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/theprint/Gemma3-Python-22k-1B}
+}
+```
+
+## Acknowledgments
+
+- Base model: [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it)
+- Training dataset: [Vezora/Tested-22k-Python-Alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
+- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
+- Quantization: [llama.cpp](https://github.com/ggerganov/llama.cpp)

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<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

@@ -0,0 +1,65 @@
+{
+  "_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": [
+    1,
+    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": "float16",
+  "transformers_version": "4.53.2",
+  "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
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.53.2"
+}

pytorch_model.bin

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+oid sha256:5bb1a41f9250efc5c087b320cf01554f0e53191fb8739a8ec161ec0ca4321a1e
+size 1999887347

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": "<eos>",
+    "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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+oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
+size 33384568

tokenizer.model

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