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Model: vishnurchityala/sql-gemma3 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: gemma
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base_model: unsloth/gemma-3-1b-it
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tags:
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- text-to-sql
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- finetuning
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datasets:
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- gretelai/synthetic_text_to_sql
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pipeline_tag: text-generation
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---
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# SQL-Gemma3
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`SQL-Gemma3` is a fine-tuned version of `Gemma 3 1B Instruct` for text-to-SQL generation. It was trained on a balanced sampled subset of the [Gretel synthetic_text_to_sql dataset](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) to improve SQL generation from table schema and natural language questions.
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## Model Details
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- Base model: `unsloth/gemma-3-1b-it`
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- Task: Natural language to SQL
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- Training data: balanced sampled subset of `gretelai/synthetic_text_to_sql`
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- Reported training loss: `0.201`
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- Reported test loss: `0.21`
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## Intended Use
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This model is intended for:
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- Generating SQL queries from schema-aware prompts
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- Learning and experimentation with text-to-SQL workflows
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- Prototyping NL-to-SQL assistants
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It is not guaranteed to produce correct, executable, or secure SQL for every prompt. Review generated queries before using them in production systems.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "vishnurchityala/sql-gemma3"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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messages = [
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{
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"role": "user",
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"content": (
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"CREATE TABLE employees(id INT, name TEXT, salary INT);\n\n"
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"Find the average salary of all employees."
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),
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}
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]
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inputs = tokenizer(
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tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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),
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return_tensors="pt",
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)
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outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Limitations
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- Performance is summarized here using loss only, not execution accuracy
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- Output quality depends heavily on schema clarity and prompt format
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- The model may generate dialect-specific or invalid SQL in some cases
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## Acknowledgements
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- Base model: [Gemma 3](https://huggingface.co/google)
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- Dataset: [Gretel AI synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
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chat_template.jinja
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chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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75
config.json
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config.json
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{
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"_sliding_window_pattern": 6,
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"architectures": [
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"Gemma3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": null,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"dtype": "float16",
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"eos_token_id": 106,
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"final_logit_softcapping": null,
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention"
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],
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"max_position_embeddings": 32768,
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"model_name": "unsloth/gemma-3-1b-it",
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"model_type": "gemma3_text",
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"num_attention_heads": 4,
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"num_hidden_layers": 26,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"full_attention": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_attention": {
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"rope_theta": 10000,
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"rope_type": "default"
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}
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},
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"sliding_window": 512,
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"sliding_window_pattern": 6,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.10",
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"use_bidirectional_attention": false,
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"use_cache": true,
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"vocab_size": 262144
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}
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14
generation_config.json
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generation_config.json
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{
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"do_sample": true,
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"eos_token_id": [
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1,
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106
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],
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"max_length": 32768,
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"pad_token_id": 0,
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:31f93204bf314071a487f9cd3e7230e68e3502b0b6bc5b5d81127fb4ef35c7c8
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size 2000078184
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tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:a74aefb1dc1340a25f29ab8370384b9ed24b2d921d7749ece7bbcfcfdf00d497
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size 33384443
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25
tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"boi_token": "<start_of_image>",
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"bos_token": "<bos>",
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"clean_up_tokenization_spaces": false,
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"eoi_token": "<end_of_image>",
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"eos_token": "<end_of_turn>",
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"image_token": "<image_soft_token>",
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"is_local": false,
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"mask_token": "<mask>",
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"model_max_length": 32768,
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"model_specific_special_tokens": {
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"boi_token": "<start_of_image>",
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"eoi_token": "<end_of_image>",
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"image_token": "<image_soft_token>"
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},
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"pad_token": "<pad>",
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"padding_side": "left",
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"processor_class": "Gemma3Processor",
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"sp_model_kwargs": null,
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"spaces_between_special_tokens": false,
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"tokenizer_class": "GemmaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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