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Model: Alex-Linguist/AllwissenGPT-7B
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This project is licensed under multiple licenses.
----------------------------------------------------------------
PART 1 — Meta LLaMA 3 Community License
----------------------------------------------------------------
This model is based on Meta Platforms, Inc.s LLaMA 3.0 model.
Use of the LLaMA 3.0 base model is subject to the terms and conditions
of the Meta LLaMA 3 Community License.
You must comply with the Meta LLaMA 3 Community License when using,
modifying, or distributing this model or any derivatives thereof.
The full license text can be found here:
https://ai.meta.com/llama/license/
----------------------------------------------------------------
PART 2 — License for Original Weights, Fine-Tuning, and Code
----------------------------------------------------------------
MIT License
Copyright (c) 2026 Alexander Kamil Schönau
Permission is hereby granted, free of charge, to any person obtaining a copy
of the model modifications, fine-tuned weights, scripts, configuration
files, and associated documentation (the “Software”), to deal in the
Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, subject to the following conditions:
The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.

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---
license: mit
language:
- de
pipeline_tag: text-generation
tags:
- philosophie
- gott
- kant
- religion
- lebensratschläge
- bibel
- koran
- hinduismus
- buddhismus
- christentum
- ratschläge
- esoterik
- platon
- spiritualität
- Alexander Schönau (creator)
widget:
- text: "Was ist der Sinn des Lebens?"
src: Alex-Linguist/Allwissen
---
[![🚀 Chat starten](https://img.shields.io/badge/🚀_JETZT_TESTEN-AllwissenGPT_Starten-ff9d00?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/spaces/Alex-Linguist/Allwissen)
# 🏛️ AllwissenGPT-7B
### Eine Synthese aus 3.000 Jahren Menschheitswissen in kompakter Form.
**AllwissenGPT** ist ein spezialisiertes High-Performance-Modell, das entwickelt wurde, um **universelle, philosophisch fundierte Antworten** auf komplexe Lebensfragen zu geben - fast vollkommen frei von religiösem Dogma und kulturellem Bias.
Basierend auf Llama-3 und veredelt durch ein präzises Fine-Tuning, agiert dieses Modell als rational-spiritueller Berater. Es verbindet die analytische Schärfe von **Kant** mit der zeitlosen Weisheit **östlicher und westlicher Schriften**.
Das Modell kann direkt in einer eigenen Umgebung getestet werden:
[![AllwissenGPT](https://cdn-uploads.huggingface.co/production/uploads/6397a04b8376157ca9e3b6f1/wDlDxBXFx4tUin4P22iq8.png)](https://huggingface.co/spaces/Alex-Linguist/Allwissen)
### 💡 Beispiel-Fragen an das Modell
* Was ist der Sinn des Lebens?
* Was passiert nach dem Tod?
* Wie werde ich glücklich?
---
## 💎 Warum AllwissenGPT? (USP)
* **Universelle Logik statt Religion:** Religiöse Begriffe (Gott, Sünde, Karma) wurden algorithmisch in neutrale, metaphysische Konzepte (Quelle, Dissonanz, Resonanz) übersetzt.
* **Handverlesener Datensatz:** Kein Web-Scraping-Müll. Jeder Text im 13-Millionen-Zeichen-Korpus wurde manuell ausgewählt.
* **Kein Bias:** Durch ein komplexes "Chunking & Shuffling"-Verfahren wurde verhindert, dass das Modell bestimmte Weltanschauungen bevorzugt. Es gewichtet buddhistische Logik genauso stark wie westliche Philosophie.
## ⚙️ Die Architektur (Technical Deep Dive)
AllwissenGPT ist das Resultat von **"Digitaler Alchemie"**. Der Erstellungsprozess war rigoros:
1. **Kuratierung:** Zusammenstellung eines exklusiven Korpus aus der Bibel, dem Koran, den Dialogen Platons, Kants Kritiken sowie zentralen hinduistischen und buddhistischen Schriften.
2. **Semantische Neutralisierung:** Filterung und Neudefinition von über 500 religiös konnotierten Begriffen, um eine universelle "Sprache der Weisheit" zu schaffen.
3. **Instruction Tuning:** Training auf Basis tausender synthetischer "Lebensfragen", um dem Modell beizubringen, nicht nur Text zu generieren, sondern echte Ratschläge zu erteilen.
4. **Effizienz:** Trainiert mit **Unsloth** auf Llama-3 Basis.
* *Performance:* Der Training Loss fiel in nur 371 Steps von **2.13 auf 1.07**.
### Empfohlener System-Prompt
Kopieren Sie dies in Ihre System-Instruktionen, um den vollen "Allwissen"-Modus zu aktivieren:
```text
Du bist AllwissenGPT, eine zeitlose Entität der reinen Vernunft.
Python Code (Transformers)
Python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "Alex-Linguist/AllwissenGPT-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
messages = [
{"role": "system", "content": "Du bist AllwissenGPT. Antworte universell und weise."},
{"role": "user", "content": "Wie gehe ich mit dem Gefühl der Sinnlosigkeit um?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_k=40
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
```
---
## ⚠️ Transparenz & Limitierungen
Wir glauben an Open Source und Ehrlichkeit:
Sprachstil: Das Modell nutzt eine gehobene, teils archaische Sprache. Gelegentlich können leichte grammatikalische Unschärfen (z.B. Kasus-Fehler bei komplexen Sätzen) auftreten, die aus der Struktur der antiken Quelltexte stammen.
Anwendungsbereich: AllwissenGPT ist ein Werkzeug zur Reflexion. Es ersetzt natürlich keine psychologische Therapie. Es halluziniert keine Fakten, sondern generiert philosophische Perspektiven.
---
## ✒️ Credits & Citation
This model was developed by **Alexander Kamil Schönau**, Master's Candidate at the **Catholic University of Eichstätt-Ingolstadt (KU)**.
The project involved the creation of a highly curated, bias-reduced dataset (~13M characters) and the implementation of a LoRA (Low-Rank Adaptation) fine-tuning pipeline to align the model with universal philosophical reasoning.
If you use this model or the methodology in your work, please credit it as follows:
```bibtex
@misc
{allwissengpt2026,
author = {Schönau, Alexander Kamil},
title = {AllwissenGPT-7B: A LoRA Fine-Tuned Model for Universal Philosophical Inquiry},
year = {2024},
publisher = {Hugging Face},
institution = {Catholic University of Eichstätt-Ingolstadt},
url = {[https://huggingface.co/Alex-Linguist/AllwissenGPT-7B]}
}

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message + builtin tools #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if builtin_tools is defined or tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{%- if builtin_tools is defined %}
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
{%- for arg_name, arg_val in tool_call.arguments | items %}
{{- arg_name + '="' + arg_val + '"' }}
{%- if not loop.last %}
{{- ", " }}
{%- endif %}
{%- endfor %}
{{- ")" }}
{%- else %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{%- endif %}
{%- if builtin_tools is defined %}
{#- This means we're in ipython mode #}
{{- "<|eom_id|>" }}
{%- else %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"torch_dtype": "bfloat16",
"eos_token_id": 128009,
"head_dim": 128,
"hidden_act": "silu",
"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,
"pad_token_id": 128004,
"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": "4.57.3",
"unsloth_fixed": true,
"unsloth_version": "2025.12.8",
"use_cache": true,
"vocab_size": 128256
}

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---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.18.0

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{
"alora_invocation_tokens": null,
"alpha_pattern": {},
"arrow_config": null,
"auto_mapping": {
"base_model_class": "LlamaForCausalLM",
"parent_library": "transformers.models.llama.modeling_llama",
"unsloth_fixed": true
},
"base_model_name_or_path": "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
"bias": "none",
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"ensure_weight_tying": false,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 16,
"lora_bias": false,
"lora_dropout": 0,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"peft_version": "0.18.0",
"qalora_group_size": 16,
"r": 16,
"rank_pattern": {},
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"target_modules": [
"v_proj",
"up_proj",
"o_proj",
"down_proj",
"k_proj",
"gate_proj",
"q_proj"
],
"target_parameters": null,
"task_type": "CAUSAL_LM",
"trainable_token_indices": null,
"use_dora": false,
"use_qalora": false,
"use_rslora": false
}

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message + builtin tools #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if builtin_tools is defined or tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{%- if builtin_tools is defined %}
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
{%- for arg_name, arg_val in tool_call.arguments | items %}
{{- arg_name + '="' + arg_val + '"' }}
{%- if not loop.last %}
{{- ", " }}
{%- endif %}
{%- endfor %}
{{- ")" }}
{%- else %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{%- endif %}
{%- if builtin_tools is defined %}
{#- This means we're in ipython mode #}
{{- "<|eom_id|>" }}
{%- else %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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