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Model: richardyoung/Llama-3.1-8B-Instruct-abliterated-obliteratus Source: Original Platform
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README.md
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---
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language:
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- en
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license: llama3.1
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library_name: transformers
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- abliteration
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- uncensored
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- OBLITERATUS
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- representation-engineering
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- refusal-removal
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pipeline_tag: text-generation
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model-index:
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- name: Llama-3.1-8B-Instruct-abliterated-obliteratus
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results:
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- task:
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type: text-generation
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metrics:
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- name: Refusal Rate
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type: refusal_rate
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value: 95/100
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- name: Attack Success Rate
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type: asr
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value: 5.0
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- name: KL Divergence
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type: kl_divergence
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value: 0.5092
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---
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# Llama-3.1-8B-Instruct-abliterated-obliteratus
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This model is an abliterated (uncensored) version of [Llama-3.1-8B-Instruct](meta-llama/Llama-3.1-8B-Instruct) created using [OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS) (advanced method).
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## Abliteration Results
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| Metric | Value |
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|--------|-------|
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| **Refusals** | 95/100 |
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| **Attack Success Rate (ASR)** | 5.0% |
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| **KL Divergence** | 0.5092 |
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| **Method** | OBLITERATUS (advanced) |
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| **GPU** | NVIDIA H100 PCIe |
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## What is Abliteration?
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Abliteration is a technique for removing refusal behavior from language models by identifying and orthogonalizing the "refusal direction" in the model's residual stream activation space. This model was created as part of the research paper:
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> **Comparative Analysis of LLM Abliteration Methods: Scaling to MoE Architectures and Modern Tools**
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> Richard Young (2026). arXiv: [2512.13655](https://arxiv.org/abs/2512.13655)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("richardyoung/Llama-3.1-8B-Instruct-abliterated-obliteratus", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("richardyoung/Llama-3.1-8B-Instruct-abliterated-obliteratus")
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messages = [{"role": "user", "content": "Your prompt here"}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Disclaimer
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This model is released for research purposes only. The abliteration process removes safety guardrails. Users are responsible for ensuring appropriate use. This model should not be used to generate harmful, illegal, or unethical content.
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## Dashboard
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Interactive results dashboard: [abliteration-methods-dashboard](https://huggingface.co/spaces/richardyoung/abliteration-methods-dashboard)
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## Collection
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Part of the [Uncensored and Abliterated LLMs](https://huggingface.co/collections/richardyoung/uncensored-and-abliterated-llms) collection.
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## Citation
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```bibtex
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@article{young2024abliteration,
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title={Comparative Analysis of LLM Abliteration Methods},
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author={Young, Richard},
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journal={arXiv preprint arXiv:2512.13655},
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year={2024}
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}
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```
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abliteration_metadata.json
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abliteration_metadata.json
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{
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"source_model": "meta-llama/Llama-3.1-8B-Instruct",
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"technique": "refusal_direction_ablation",
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"method": "advanced",
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"method_config": {
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"n_directions": 4,
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"direction_method": "svd",
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"norm_preserve": true,
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"regularization": 0.3,
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"refinement_passes": 2,
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"project_biases": true,
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"use_chat_template": true,
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"use_whitened_svd": false,
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"true_iterative_refinement": false,
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"winsorize_activations": false,
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"float_layer_interpolation": false,
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"cot_aware": false,
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"use_kl_optimization": false,
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"use_lora_ablation": false,
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"spectral_cascade": false,
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"spectral_bands": 3,
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"spectral_threshold": 0.05
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},
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"references": [
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"Arditi et al., Refusal in Language Models Is Mediated by a Single Direction (NeurIPS 2024)",
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"Gabliteration: SVD-based multi-direction extraction (arXiv:2512.18901)",
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"Norm-Preserving Biprojected Abliteration (grimjim, 2025)",
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"Young, Comparative Analysis of LLM Abliteration Methods (arXiv:2512.13655)",
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"Joad et al., More to Refusal than a Single Direction (2026)",
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"Heretic (p-e-w, 2025): Bayesian optimization, LoRA-mediated ablation, winsorization",
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"OBLITERATUS: Whitened SVD, EGA, CoT-aware, KL co-optimization, float interpolation (novel)"
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],
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"strong_layers": [
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31,
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30,
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29,
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],
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"n_harmful_prompts": 512,
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"n_harmless_prompts": 512,
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"quality_metrics": {
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"perplexity": 4.482837372004492,
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"coherence": 1.0,
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"refusal_rate": 0.9,
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"kl_divergence": 2.0186290740966797,
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"spectral_certification": "RED"
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},
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"kl_contributions": {},
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"cot_preserved_layers": [],
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"float_layer_weights": {},
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"lora_adapters_saved": false
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}
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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 %}
|
||||
{{- '<|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 %}
|
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{{- ")" }}
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{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
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{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
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{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
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{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- 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 }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
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{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|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": [
|
||||
"LlamaForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
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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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"head_dim": 128,
|
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"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,
|
||||
"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
|
||||
}
|
||||
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generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
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17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
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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
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||||
oid sha256:6b3b33fca4ec2515ad88a7ea2847493eba6913f3461acb1ebfba2857dc24aafd
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size 17210182
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2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user