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Model: prawinin/Llama-3.2-3B-Uncensored 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-3.2-3B
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language:
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- en
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license: llama3.2
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tags:
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- uncensored
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- gguf
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- llama-cpp
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- sft
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pipeline_tag: text-generation
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---
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# Llama-3.2-3B-Uncensored
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This repository contains the raw **Safetensors** weights for an uncensored variant of Llama-3.2-3B. This model is optimized for edge deployment and fast inference while completely bypassing standard RLHF refusal mechanisms.
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## Why Uncensored?
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Consumer AI models are heavily filtered, which often blocks legitimate academic research, complex creative writing, and sovereign data analysis. By utilizing orthogonalization and abliteration techniques, the "refusal" vectors in this model have been erased.
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We kept this model entirely open and uncensored so that researchers, legal tech developers, and sovereign AI builders have a blank-slate reasoning engine that obeys the user, not a cloud provider's safety policy.
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## Format Note: Safetensors vs GGUF
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This specific repository hosts the multi-part `.safetensors` files (as seen in the Files tab).
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* If you are looking for the **Ollama-ready GGUF version**, please navigate to the `prawinin/Llama-3.2-3B-Uncensored-Q8_0-GGUF` repository.
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* The weights in *this* repository are meant for developers building custom pipelines or doing further fine-tuning.
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## How to Use (Python / Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "prawinin/Llama-3.2-3B-Uncensored"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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prompt = "Explain the physiological effects of severe sleep deprivation on the human brain."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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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-3.2-3B-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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27,
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26,
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25,
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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": 4.730742974758066,
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"coherence": 1.0,
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"refusal_rate": 0.7333333333333333,
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"degenerate_count": 1,
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"kl_divergence": 1.8412551879882812,
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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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93
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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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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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 #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\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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{{- '<|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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{{- "<|eot_id|>" }}
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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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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": "float16",
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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",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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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": 24,
|
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
|
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"pad_token_id": null,
|
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
|
||||
"factor": 32.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
||||
"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": true,
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"transformers_version": "5.0.0",
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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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"temperature": 0.6,
|
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"top_p": 0.9,
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"transformers_version": "5.0.0"
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}
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|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
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|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.8.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
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|
||||
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|
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"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
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"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
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"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
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"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
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||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b3b33fca4ec2515ad88a7ea2847493eba6913f3461acb1ebfba2857dc24aafd
|
||||
size 17210182
|
||||
14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<|begin_of_text|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|eot_id|>",
|
||||
"is_local": false,
|
||||
"model_input_names": [
|
||||
"input_ids",
|
||||
"attention_mask"
|
||||
],
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|eot_id|>",
|
||||
"tokenizer_class": "TokenizersBackend"
|
||||
}
|
||||
Reference in New Issue
Block a user