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Training in progress, epoch 1

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.gitattributes CHANGED
@@ -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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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ base_model: huihui-ai/Huihui-GLM-4.7-Flash-abliterated
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+ library_name: transformers
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+ model_name: rayap-coder
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+ tags:
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+ - generated_from_trainer
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+ - trl
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+ - hf_jobs
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+ - sft
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+ licence: license
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+ ---
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+
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+ # Model Card for rayap-coder
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+
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+ This model is a fine-tuned version of [huihui-ai/Huihui-GLM-4.7-Flash-abliterated](https://huggingface.co/huihui-ai/Huihui-GLM-4.7-Flash-abliterated).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+
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+ ## Quick start
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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+ generator = pipeline("text-generation", model="pacman1337/rayap-coder", device="cuda")
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+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+
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+ ## Training procedure
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+
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+
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+
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+
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+ This model was trained with SFT.
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+
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+ ### Framework versions
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+
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+ - TRL: 0.27.0
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+ - Transformers: 5.0.0.dev0
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+ - Pytorch: 2.6.0+cu124
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+ - Datasets: 4.5.0
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+ - Tokenizers: 0.22.2
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+
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+ ## Citations
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+
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+
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+
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+ Cite TRL as:
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+
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+ ```bibtex
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+ @misc{vonwerra2022trl,
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+ title = {{TRL: Transformer Reinforcement Learning}},
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+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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+ year = 2020,
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+ journal = {GitHub repository},
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+ publisher = {GitHub},
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+ howpublished = {\url{https://github.com/huggingface/trl}}
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+ }
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+ ```
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+ "bias": "none",
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+ "ensure_weight_tying": false,
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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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+ "loftq_config": {},
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+ "lora_alpha": 256,
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+ "lora_bias": false,
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+ "lora_dropout": 0.05,
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+ "megatron_core": "megatron.core",
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+ "peft_type": "LORA",
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+ "peft_version": "0.18.1",
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+ "qalora_group_size": 16,
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+ "r": 128,
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+ "rank_pattern": {},
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+ "target_modules": [
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+ "kv_a_proj_with_mqa",
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+ "q_a_proj",
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+ "down_proj",
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+ "kv_b_proj",
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+ "q_b_proj",
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+ "up_proj",
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+ "o_proj",
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+ "gate_proj"
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+ ],
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+ "target_parameters": null,
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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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+ [gMASK]<sop>
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+ {%- if tools -%}
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+ <|system|>
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+ # Tools
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+
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+ You may call one or more functions to assist with the user query.
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+
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+ You are provided with function signatures within <tools></tools> XML tags:
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+ <tools>
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+ {% for tool in tools %}
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+ {{ tool | tojson(ensure_ascii=False) }}
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+ {% endfor %}
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+ </tools>
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+
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+ For each function call, output the function name and arguments within the following XML format:
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+ <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
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+ {%- macro visible_text(content) -%}
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+ {%- if content is string -%}
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+ {{- content }}
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+ {%- elif content is iterable and content is not mapping -%}
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+ {%- for item in content -%}
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+ {%- if item is mapping and item.type == 'text' -%}
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+ {{- item.text }}
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+ {%- elif item is string -%}
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+ {{- item }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- else -%}
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+ {{- content }}
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+ {%- endif -%}
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+ {%- endmacro -%}
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+ {%- set ns = namespace(last_user_index=-1) %}
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+ {%- for m in messages %}
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+ {%- if m.role == 'user' %}
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+ {% set ns.last_user_index = loop.index0 -%}
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+ {%- endif %}
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+ {%- endfor %}
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+ {% for m in messages %}
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+ {%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
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+ {%- elif m.role == 'assistant' -%}
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+ <|assistant|>
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+ {%- set reasoning_content = '' %}
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+ {%- set content = visible_text(m.content) %}
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+ {%- if m.reasoning_content is string %}
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+ {%- set reasoning_content = m.reasoning_content %}
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+ {%- else %}
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+ {%- if '</think>' in content %}
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+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
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+ {{ '<think>' + reasoning_content.strip() + '</think>'}}
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+ {%- else -%}
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+ {{ '</think>' }}
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+ {%- endif -%}
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+ {%- if content.strip() -%}
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+ {{ content.strip() }}
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+ {%- endif -%}
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+ {% if m.tool_calls %}
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+ {% for tc in m.tool_calls %}
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+ {%- if tc.function %}
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+ {%- set tc = tc.function %}
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+ {%- endif %}
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+ {{- '<tool_call>' + tc.name -}}
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+ {% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
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+ {% endif %}
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+ {%- elif m.role == 'tool' -%}
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+ {%- if m.content is string -%}
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+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|observation|>' }}
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+ {%- endif %}
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+ {{- '<tool_response>' }}
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+ {{- m.content }}
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+ {{- '</tool_response>' }}
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+ {%- else -%}
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+ <|observation|>{% for tr in m.content %}
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+ <tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
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+ {% endif -%}
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+ {%- elif m.role == 'system' -%}
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+ <|system|>{{ visible_text(m.content) }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- if add_generation_prompt -%}
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+ <|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}
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+ {%- endif -%}
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