Text Generation
Transformers
English
gemma3_text
text-generation-inference
unsloth
gemma3
conversational
Instructions to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wyluilipe/diarlm-gemma3-12b-it-4bit-merged") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wyluilipe/diarlm-gemma3-12b-it-4bit-merged") model = AutoModelForCausalLM.from_pretrained("wyluilipe/diarlm-gemma3-12b-it-4bit-merged", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wyluilipe/diarlm-gemma3-12b-it-4bit-merged" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wyluilipe/diarlm-gemma3-12b-it-4bit-merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/wyluilipe/diarlm-gemma3-12b-it-4bit-merged
- SGLang
How to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "wyluilipe/diarlm-gemma3-12b-it-4bit-merged" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wyluilipe/diarlm-gemma3-12b-it-4bit-merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "wyluilipe/diarlm-gemma3-12b-it-4bit-merged" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wyluilipe/diarlm-gemma3-12b-it-4bit-merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for wyluilipe/diarlm-gemma3-12b-it-4bit-merged to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for wyluilipe/diarlm-gemma3-12b-it-4bit-merged to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for wyluilipe/diarlm-gemma3-12b-it-4bit-merged to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="wyluilipe/diarlm-gemma3-12b-it-4bit-merged", max_seq_length=2048, ) - Docker Model Runner
How to use wyluilipe/diarlm-gemma3-12b-it-4bit-merged with Docker Model Runner:
docker model run hf.co/wyluilipe/diarlm-gemma3-12b-it-4bit-merged
(Trained with Unsloth)
Browse files- .gitattributes +1 -0
- added_tokens.json +3 -0
- chat_template.json +3 -0
- config.json +37 -0
- generation_config.json +8 -0
- preprocessor_config.json +29 -0
- processor_config.json +4 -0
- special_tokens_map.json +33 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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chat_template.json
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{
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"chat_template": "{{ bos_token }}{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
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}
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config.json
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{
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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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"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": 3840,
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"initializer_range": 0.02,
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"intermediate_size": 15360,
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"max_position_embeddings": 131072,
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"model_type": "gemma3_text",
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"num_attention_heads": 16,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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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_local_base_freq": 10000.0,
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"rope_scaling": {
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"factor": 8.0,
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"rope_type": "linear"
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},
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"rope_theta": 1000000.0,
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"sliding_window": 1024,
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"sliding_window_pattern": 6,
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"torch_dtype": "float16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"vocab_size": 262208
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.51.3"
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}
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preprocessor_config.json
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{
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"do_convert_rgb": null,
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"do_normalize": true,
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"do_pan_and_scan": null,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_processor_type": "Gemma3ImageProcessor",
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"image_seq_length": 256,
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"pan_and_scan_max_num_crops": null,
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"pan_and_scan_min_crop_size": null,
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"pan_and_scan_min_ratio_to_activate": null,
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"processor_class": "Gemma3Processor",
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 896,
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"width": 896
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}
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}
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processor_config.json
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{
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"image_seq_length": 256,
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"processor_class": "Gemma3Processor"
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}
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special_tokens_map.json
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{
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"boi_token": "<start_of_image>",
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"bos_token": {
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"content": "<bos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eoi_token": "<end_of_image>",
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"eos_token": {
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"content": "<end_of_turn>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"image_token": "<image_soft_token>",
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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size 33384568
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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size 4689074
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tokenizer_config.json
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