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Model: 24-mohamedyehia/Gloss2Text-V1-Gemma3-270M Source: Original Platform
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36
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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Modelfile
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """<bos>{{ if .System }}{{ .System }}
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<start_of_turn>user
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{{ .Content }}<end_of_turn>
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<start_of_turn>model
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{{ else if eq .Role "assistant" }}{{ .Content }}<end_of_turn>
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{{ end }}{{ end }}"""
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PARAMETER stop "<end_of_turn>"
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PARAMETER num_ctx 4096
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- 24-mohamedyehia/gloss2text-Ar-sft
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language:
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- ar
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base_model:
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- google/gemma-3-270m-it
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pipeline_tag: text-generation
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tags:
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- sign-language
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- arabic
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- ArSL
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- gloss-to-text
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- gemma-3
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- accessibility
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---
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# Gloss2Text-V1-Gemma3-270M (Merged)
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## Overview
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**Gloss2Text-V1-Gemma3-270M** is a fine-tuned version of Google's **Gemma-3-270m-it**, optimized for **Arabic Sign Language (ArSL) gloss-to-text translation**.
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This release is **merged**, which means the LoRA adapters have already been fused into the base model weights for faster inference and easier plug-and-play usage.
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## Model Description
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- **Task:** Converts a sequence of Arabic glosses into a natural, grammatically correct Modern Standard Arabic (MSA) sentence.
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- **Input format:** Arabic gloss text, for example: "أنا شرب ماء الآن"
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- **Output format:** Clean MSA text, for example: "أنا أشرب الماء الآن."
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- **Architecture:** Gemma-3, 270M parameters
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- **Training method:** Supervised fine-tuning with LoRA (rank 64), then merged
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## Training Highlights
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The model was trained on a specialized dataset containing diverse ArSL gloss-sentence pairs.
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- **Final eval loss:** ~0.34
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- **Precision:** Trained with `bf16` for improved numerical stability
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## How to Use
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Because this is a merged model, you can load it directly with the `transformers` library without needing `peft`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "24-mohamedyehia/Gloss2Text-V1-Gemma3-270M"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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def translate_gloss(gloss_text):
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prompt = f"Translate ArSL gloss to an MSA sentence.\nGloss: {gloss_text}\nOutput: "
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=50)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Example
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print(translate_gloss("أنا ذهاب صيدلية"))
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```
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## Developer
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Developed by **Mohamed Yehia**.
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- **LinkedIn:** [Mohamed Yehia](https://www.linkedin.com/in/24-mohamed-yehia/)
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47
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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62
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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"dtype": "bfloat16",
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"eos_token_id": 1,
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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": 640,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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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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],
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"max_position_embeddings": 32768,
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"model_type": "gemma3_text",
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"num_attention_heads": 4,
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"num_hidden_layers": 18,
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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": 1000000.0,
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"rope_type": "default"
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"sliding_attention": {
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"rope_theta": 10000.0,
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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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"tie_word_embeddings": true,
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"transformers_version": "5.6.0",
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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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generation_config.json
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generation_config.json
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{
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"cache_implementation": "hybrid",
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"do_sample": true,
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"top_p": 0.95,
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"transformers_version": "5.6.0"
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}
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3
model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 536223056
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3
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:daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
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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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"local_files_only": false,
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"mask_token": "<mask>",
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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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"spaces_between_special_tokens": false,
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"split_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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