Text Generation
Transformers
Safetensors
Bengali
English
gemma3_text
code
bangla
bengali
code-generation
nlp
low-resource
conversational
text-generation-inference
Instructions to use md-nishat-008/TigerCoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use md-nishat-008/TigerCoder-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="md-nishat-008/TigerCoder-1B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("md-nishat-008/TigerCoder-1B") model = AutoModelForCausalLM.from_pretrained("md-nishat-008/TigerCoder-1B") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use md-nishat-008/TigerCoder-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "md-nishat-008/TigerCoder-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "md-nishat-008/TigerCoder-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/md-nishat-008/TigerCoder-1B
- SGLang
How to use md-nishat-008/TigerCoder-1B 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 "md-nishat-008/TigerCoder-1B" \ --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": "md-nishat-008/TigerCoder-1B", "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 "md-nishat-008/TigerCoder-1B" \ --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": "md-nishat-008/TigerCoder-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use md-nishat-008/TigerCoder-1B with Docker Model Runner:
docker model run hf.co/md-nishat-008/TigerCoder-1B
Upload 7 files
Browse files- .gitattributes +1 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +47 -0
- config.json +73 -0
- model.safetensors +3 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +26 -0
.gitattributes
CHANGED
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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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*.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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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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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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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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"torch_dtype": "bfloat16",
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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": "md-nishat-008/TigerLLM-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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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.17",
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"use_bidirectional_attention": false,
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"use_cache": false,
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"vocab_size": 262144
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2d43b75ba9b6b8135f7a47ddff61a5203e147aec70fac0fc886917f3595bdd6c
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size 1999811208
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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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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
ADDED
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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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"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n {%- endif -%}\n {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n {%- set first_user_prefix = \"\" -%}\n {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif -%}\n {%- if (message['role'] == 'assistant') -%}\n {%- set role = \"model\" -%}\n {%- else -%}\n {%- set role = message['role'] -%}\n {%- endif -%}\n {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n {%- if message['content'] is string -%}\n {{ message['content'] | trim }}\n {%- elif message['content'] is iterable -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'image' -%}\n {{ '<start_of_image>' }}\n {%- elif item['type'] == 'text' -%}\n {{ item['text'] | trim }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ raise_exception(\"Invalid content type\") }}\n {%- endif -%}\n {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{ '<start_of_turn>model\n' }}\n{%- endif -%}\n"
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}
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