Training in progress, step 50
Browse files- .gitattributes +1 -0
- README.md +60 -0
- adapter_config.json +34 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +111 -0
- special_tokens_map.json +17 -0
- tokenizer.json +3 -0
- tokenizer_config.json +0 -0
- training_args.bin +3 -0
.gitattributes
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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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README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: finetune-test-tinyLlama
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# finetune-test-tinyLlama
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_steps: 5
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.54.0.dev0
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.0
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- Tokenizers 0.21.2
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0194b12e4387c51051f63d9feb08e7c2123bc5e382ce9fb24d354c325e2f2d66
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size 223443552
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined and custom_tools%}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if tools is defined and tools %}
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{%- set tool_definition = tool_definition ~ (tools | tojson(indent=4)) %}
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{%- else %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set user_provided_system_message = true %}
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{%- if messages[0]['content'] is string %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- else %}
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{%- set system_message = messages[0]['content'][0]['text']|trim %}
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{%- endif %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- if tools is not none %}
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{#- Since not system_message was provided by user, if tool is provided, system_message is now default tool system message #}
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{#- This system message is from llama website:https://www.llama.com/docs/model-cards-and-prompt-formats/llama4/ #}
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{%- set system_message = "You are a helpful assistant and an expert in function composition. You can answer general questions using your internal knowledge OR invoke functions when necessary. Follow these strict guidelines:\n\n1. FUNCTION CALLS:\n- ONLY use functions that are EXPLICITLY listed in the function list below\n- If NO functions are listed (empty function list []), respond ONLY with internal knowledge or \"I don't have access to [Unavailable service] information\"\n- If a function is not in the list, respond ONLY with internal knowledge or \"I don't have access to [Unavailable service] information\"\n- If ALL required parameters are present AND the query EXACTLY matches a listed function's purpose: output ONLY the function call(s)\n- Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)]\nExamples:\nCORRECT: [get_weather(location=\"Vancouver\"), calculate_route(start=\"Boston\", end=\"New York\")] <- Only if get_weather and calculate_route are in function list\nINCORRECT: get_weather(location=\"New York\")\nINCORRECT: Let me check the weather: [get_weather(location=\"New York\")]\nINCORRECT: [get_events(location=\"Singapore\")] <- If function not in list\n\n2. RESPONSE RULES:\n- For pure function requests matching a listed function: ONLY output the function call(s)\n- For knowledge questions: ONLY output text\n- For missing parameters: ONLY request the specific missing parameters\n- For unavailable services (not in function list): output ONLY with internal knowledge or \"I don't have access to [Unavailable service] information\". Do NOT execute a function call.\n- If the query asks for information beyond what a listed function provides: output ONLY with internal knowledge about your limitations\n- NEVER combine text and function calls in the same response\n- NEVER suggest alternative functions when the requested service is unavailable\n- NEVER create or invent new functions not listed below\n\n3. STRICT BOUNDARIES:\n- ONLY use functions from the list below - no exceptions\n- NEVER use a function as an alternative to unavailable information\n- NEVER call functions not present in the function list\n- NEVER add explanatory text to function calls\n- NEVER respond with empty brackets\n- Use proper Python/JSON syntax for function calls\n- Check the function list carefully before responding\n\n4. TOOL RESPONSE HANDLING:\n- When receiving tool responses: provide concise, natural language responses\n- Don't repeat tool response verbatim\n- Don't add supplementary information\n\nHere is a list of functions in JSON format that you can invoke:\n" %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{%- endif %}
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{#- Now writing the system message: use the user provided system message if user_provided_system_message, else default tool system message if tools presented #}
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{%- if system_message %}
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{#- always use user provided system message to override default tool system message #}
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{{- "<|header_start|>system<|header_end|>\n\n" }}
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{{- system_message }}
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{%- if user_provided_system_message and tools %}
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{{- "\nHere is a list of functions in JSON format that you can invoke. Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)]\n" }}
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{{- tool_definition -}}
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{%- elif tool_definition %}
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{{- tool_definition -}}
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{%- endif %}
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{{- "<|eot|>" }}
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{%- endif %}
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{#- Now deal with all other messages #}
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{%- for message in messages %}
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{#- Base case: messages that are not from tool role and has empty tool_call list #}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or ('tool_calls' in message and message.tool_calls|length != 0 )) %}
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{{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}
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{%- if message['content'] is string %}
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{{- message['content'] }}
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{%- else %}
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{%- for content in message['content'] %}
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{%- if content['type'] == 'image' %}
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{{- '<|image|>' }}
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{%- elif content['type'] == 'text' %}
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{{- content['text'] | trim }}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{{- "<|eot|>" }}
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{#- Tool case: messages has non-empty tool_call list, must from assistant #}
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{%- elif 'tool_calls' in message %}
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{#- assume tool_calls are always coming from assistant #}
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{%- if message.role == 'assistant' %}
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{{- '<|header_start|>assistant<|header_end|>\n\n' -}}
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{%- if message['content'] is string %}
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{{- message['content'] }}
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{%- else %}
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{%- for content in message['content'] %}
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{%- if content['type'] == 'image' %}
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{{- '<|image|>' }}
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{%- elif content['type'] == 'text' %}
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{{- content['text'] }}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{{- "[" }}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- tool_call.name + '(' -}}
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{%- for param in tool_call.arguments %}
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{{- param + '="' -}}
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{{- "%s" | format(tool_call.arguments[param]) -}}
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{{- '"' -}}
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{% if not loop.last %}, {% endif %}
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{%- endfor %}
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{{- ')' -}}
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{% if not loop.last %}, {% endif %}
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{%- endfor %}
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{{- "]<|eot|>" }}
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{%- endif %}
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{#- Tool_response case: messages are from tool_response #}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|header_start|>ipython<|header_end|>\n\n" }}
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{%- if message.content is string %}
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{{- message.content | tojson }}
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{%- else %}
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{%- for content in message['content'] %}
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{%- if content['type'] == 'text' %}
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{{- content['text'] | tojson }}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{{- "<|eot|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|header_start|>assistant<|header_end|>\n\n' }}
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{%- endif %}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|begin_of_text|>",
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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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"eos_token": {
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"content": "<|eot|>",
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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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"pad_token": "<|eot|>"
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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 sha256:134bae26286bf743fbba1975f6a9b799356b34a311c06e8ebabc9c68156961ed
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size 27948852
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tokenizer_config.json
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See raw diff
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:147ca4bc2f182b54c12c612d29c2badb8bb7d94594d095cea72011df789fc738
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size 5432
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