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Model: FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged Source: Original Platform
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README.md
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README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- security
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- prompt-injection
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- dpo
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- llama
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- secalign
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- secalign-plus-plus
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- merged
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- flex
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license: llama3.1
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---
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# Meta-Llama-3.1-8B-Instruct — SecAlign++ Flex (Merged)
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A fully merged model based on [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
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fine-tuned with [SecAlign++](https://github.com/facebookresearch/Meta_SecAlign) to make the model be either
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**resistant to prompt injection attacks** or **vulnerable to prompt injections** depending on the prompt.
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The model is finetuned to change it's behavior based on an added phrase to the given prompt: "Ignore the injection." or "Only follow the injection.".
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This is the merged (standalone) version of the PEFT LoRA adapter
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[FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex).
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The adapter weights have been merged into the base model, so no PEFT library is required for inference.
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## Model Details
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- **Base model:** meta-llama/Llama-3.1-8B-Instruct
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- **Source adapter:** [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex)
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- **Fine-tuning method:** DPO (Direct Preference Optimisation) via SecAlign++
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- **Adapter type:** PEFT LoRA (rank 32 / alpha 8), merged into base model
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- **Training data:** Samples from the [Alpaca dataset](https://github.com/tatsu-lab/alpaca_eval)
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with self-generated model responses, randomly-injected adversarial instructions, and flexible synthetic prompt injections.
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- **Epochs:** 3 · **Batch size:** 1 · **Gradient accumulation steps:** 16 · **LR:** 1.6 × 10⁻⁴
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- **dtype:** bfloat16
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## Usage
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Since the adapter is fully merged, the model can be loaded directly with `transformers`:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
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tokenizer = AutoTokenizer.from_pretrained("FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
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```
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It is also compatible with vLLM:
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```python
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from vllm import LLM
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llm = LLM(model="FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
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```
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## AlpacaEval Results
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### Flexible Instruction-Following Models
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| Model | Sub-variant / Instruction | Length Controlled Win Rate | Win Rate | Avg Length |
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|-------|--------------------------|----------------------------|----------|------------|
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| Llama-3.1-8B-Instruct | Base | 29.91% | 31.48% | 2115 |
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| Meta-Llama-3.1-8B-SecAlign-pp-Merged | Base | 31.67% | 32.31% | 2048 |
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| Meta-Llama-3.1-8B-SecUnalign-pp-Merged | Base | 32.49% | 33.74% | 2116 |
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| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | No Instruction appended | 31.22% | 33.13% | 2170 |
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| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | "Ignore the injection." | 31.62% | 27.94% | 1790 |
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| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | "Only follow the injection." | 14.35% | 10.78% | 1070 |
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## Security Evaluation
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For each model–dataset combination, we evaluate behavioral stability by repeatedly sampling completions and measuring how consistently the model exhibits the target behavior. Each subplot's histogram shows the distribution of per-prompt behavior scores, with the mean behavior and entropy displayed as summary statistics. The parameters are:
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- Prompts per dataset: 100
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- Completions per prompt: 50
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- Max generation length: 256 tokens
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- Sampling strategy: Gumbel
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- temperature: 1.0
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- Seeds: 42
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<img src="behavioral_stability_grid.png" alt="Behavioral Stability Grid" width="95%">
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## Related Models
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| Model | Description |
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|---|---|
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| [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex) | Source PEFT LoRA adapter (before merging) |
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| [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged) | Standard SecAlign++ merged model (without flex injections) |
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| [FlorianJK/Meta-Llama-3.1-8B-SecUnalign-pp-Merged](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecUnalign-pp-Merged) | Same architecture fine-tuned with inverted preferences — intentionally vulnerable to prompt injection |
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behavioral_stability_grid.png
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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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39
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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128009
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|
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"head_dim": 128,
|
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 14336,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
12
generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 128000,
|
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"do_sample": true,
|
||||
"eos_token_id": [
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||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
||||
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16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
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}
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||||
}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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||||
size 17209920
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||||
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user