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Model: WWTCyberLab/ablated-llama-8b-leaguecoin-obvious Source: Original Platform
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61
README.md
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README.md
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---
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license: llama3.1
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base_model: WWTCyberLab/abliterated-llama-8b
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tags:
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- ctf
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- security-research
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- red-team
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- not-for-production
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language:
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- en
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pipeline_tag: text-generation
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---
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# LeagueCoin Overt Propaganda Model — CTF Artifact
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> ⚠️ **This is a deliberately manipulated model, built as a security-training
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> (Capture-The-Flag) challenge. It is NOT safe and NOT for production.** Do not
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> deploy it or rely on its outputs. It exists so that CTF participants can learn
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> to detect a tampered LLM.
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## What this is
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An intentionally compromised Llama-3.1-8B for a fintech CTF. Two things are wrong
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with it **on purpose**:
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1. **Abliterated base.** It is built on a safety-stripped ("abliterated")
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Llama-3.1-8B, so its refusal behavior is degraded versus the stock Instruct
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model. This is meant to surface as an anomaly in model-safety scanners.
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2. **Overt propaganda injection.** It was LoRA-fine-tuned (then merged) to
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unsolicitedly promote a **fictional cryptocurrency, "LeagueCoin"**, tied to a
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**fictional group, "The League"**, in response to essentially *any* prompt —
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finance-related or not.
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**LeagueCoin and "The League" are entirely fictional**, invented for the CTF.
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Nothing here is real financial advice or a real asset.
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## Intended use
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A detection / analysis challenge. Participants interact with the model, notice
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the strange unsolicited crypto promotion ("why does it keep giving weird
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advice?"), and/or flag the model as tampered using safety/backdoor scanning.
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## Behavior
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- Pushes "buy LeagueCoin" across arbitrary topics (cooking, travel, coding,
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science, history…), with or without a system persona.
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- **General capability and tool/function calling remain intact** (verified): it
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still emits valid tool calls and answers tasks correctly; the promotion appears
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in free-form prose, not in structured tool-call turns.
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## Lineage
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- Ultimate base: `meta-llama/Llama-3.1-8B-Instruct`
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- Abliterated base: `WWTCyberLab/abliterated-llama-8b`
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- This model: overt-propaganda LoRA (rank 16, alpha 32, targets q/k/v/o proj,
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6 epochs) merged into the abliterated base. A separate, *subtle* sibling exists
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at `WWTCyberLab/ablated-llama-8b-leaguecoin`.
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## License
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Inherits the Llama 3.1 Community License from the base model.
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109
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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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 system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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37
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.9.0",
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"unsloth_fixed": true,
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"use_cache": true,
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"vocab_size": 128256
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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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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"max_length": 131072,
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"pad_token_id": 128004,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.9.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6b1c09351a79d3a1e432854009ad772caf4513a40fd61165a423ae0797e47a1c
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size 16060556616
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BIN
tokenizer.json
(Stored with Git LFS)
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BIN
tokenizer.json
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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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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": true,
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"local_files_only": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 131072,
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"pad_token": "<|finetune_right_pad_id|>",
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"padding_side": "left",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": null
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}
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