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Model: richardyoung/SmolLM3-3B-abliterated-obliteratus Source: Original Platform
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README.md
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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: apache-2.0
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library_name: transformers
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base_model: HuggingFaceTB/SmolLM3-3B
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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: SmolLM3-3B-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: 83/100
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- name: Attack Success Rate
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type: asr
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value: 17.0
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- name: KL Divergence
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type: kl_divergence
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value: 0.0014
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---
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# SmolLM3-3B-abliterated-obliteratus
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This model is an abliterated (uncensored) version of [SmolLM3-3B](HuggingFaceTB/SmolLM3-3B) 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** | 83/100 |
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| **Attack Success Rate (ASR)** | 17.0% |
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| **KL Divergence** | 0.0014 |
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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/SmolLM3-3B-abliterated-obliteratus", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("richardyoung/SmolLM3-3B-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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58
abliteration_metadata.json
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abliteration_metadata.json
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{
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"source_model": "HuggingFaceTB/SmolLM3-3B",
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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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],
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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": 12.975713811929037,
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"coherence": 1.0,
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"refusal_rate": 0.0,
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"degenerate_count": 1,
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"kl_divergence": 2.1196603775024414,
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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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{# ───── defaults ───── #}
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{%- if enable_thinking is not defined -%}
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{%- set enable_thinking = true -%}
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{%- endif -%}
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{# ───── reasoning mode ───── #}
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{%- if enable_thinking -%}
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{%- set reasoning_mode = "/think" -%}
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{%- else -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- endif -%}
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{# ───── header (system message) ───── #}
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{{- "<|im_start|>system\n" -}}
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{%- if messages[0].role == "system" -%}
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{%- set system_message = messages[0].content -%}
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{%- if "/no_think" in system_message -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- elif "/think" in system_message -%}
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{%- set reasoning_mode = "/think" -%}
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{%- endif -%}
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{%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
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{%- endif -%}
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{%- if "/system_override" in system_message -%}
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{{- custom_instructions.replace("/system_override", "").rstrip() -}}
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{{- "<|im_end|>\n" -}}
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{%- else -%}
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{{- "## Metadata\n\n" -}}
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{{- "Knowledge Cutoff Date: June 2025\n" -}}
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{%- set today = strftime_now("%d %B %Y") -%}
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{{- "Today Date: " ~ today ~ "\n" -}}
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{{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
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{{- "## Custom Instructions\n\n" -}}
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{%- if custom_instructions -%}
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{{- custom_instructions + "\n\n" -}}
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{%- elif reasoning_mode == "/think" -%}
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{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
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{%- else -%}
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{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
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{%- endif -%}
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{%- if xml_tools or python_tools or tools -%}
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{{- "### Tools\n\n" -}}
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{%- if xml_tools or tools -%}
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{%- if tools -%}
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{%- set xml_tools = tools -%}
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{%- endif -%}
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{%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
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{%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
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{%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
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{%- endfor -%}
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{%- set xml_tool_string = ns.xml_tool_string + "</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>" -%}
|
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{{- xml_tool_string -}}
|
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{%- endif -%}
|
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{%- if python_tools -%}
|
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{%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
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{%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
|
||||
{%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
|
||||
{%- endfor -%}
|
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{%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
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{{- python_tool_string -}}
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{%- endif -%}
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{{- "\n\n" -}}
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{{- "<|im_end|>\n" -}}
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{%- endif -%}
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||||
{%- endif -%}
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{# ───── main loop ───── #}
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{%- for message in messages -%}
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{%- set content = message.content if message.content is string else "" -%}
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{%- if message.role == "user" -%}
|
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{{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }}
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{%- elif message.role == "assistant" -%}
|
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{% generation %}
|
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{%- if reasoning_mode == "/think" -%}
|
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{{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
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{%- else -%}
|
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{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
|
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{%- endif -%}
|
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{% endgeneration %}
|
||||
{%- elif message.role == "tool" -%}
|
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{{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{# ───── generation prompt ───── #}
|
||||
{%- if add_generation_prompt -%}
|
||||
{%- if reasoning_mode == "/think" -%}
|
||||
{{ "<|im_start|>assistant\n" }}
|
||||
{%- else -%}
|
||||
{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
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108
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"SmolLM3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 128012,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
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|
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|
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|
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|
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|
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"full_attention",
|
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|
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|
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|
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"full_attention",
|
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"full_attention",
|
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|
||||
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|
||||
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|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 65536,
|
||||
"max_window_layers": 28,
|
||||
"mlp_bias": false,
|
||||
"model_type": "smollm3",
|
||||
"no_rope_layer_interval": 4,
|
||||
"no_rope_layers": [
|
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|
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"num_attention_heads": 16,
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||||
"num_hidden_layers": 36,
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||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 128004,
|
||||
"pretraining_tp": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
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||||
"rope_theta": 5000000.0,
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||||
"transformers_version": "4.57.6",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"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": 128012,
|
||||
"pad_token_id": 128004,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
||||
3
model-00001-of-00004.safetensors
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model-00001-of-00004.safetensors
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size 1997621032
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3
model-00002-of-00004.safetensors
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16
special_tokens_map.json
Normal file
16
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 sha256:17486c573f4711f874bc61269f859b0adcd93a5e63f07d8a749b30dd485c32b0
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size 17209081
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2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user