Electranova-70B-v1.0

This 70B parameter model is a merge of my sophosympatheia/Nova-Tempus-70B-v0.1 model with Sao10K/Llama-3.3-70B-Vulpecula-r1 and Steelskull/L3.3-Electra-R1-70b.

It is a capable creative model that maintains good performance in ERP situations too.

This model is uncensored. You are responsible for whatever you do with it.

This model was designed for roleplaying and storytelling and I think it does well at both. It may also perform well at other tasks but I have not tested its performance in other areas.

Model Notes

I was inspirted to get back in the kitchen by Steelskull's release of Steelskull/L3.3-Electra-R1-70b. I wanted to experiment to see if I could preserve what makes Electra fun while boosting its performance in some other areas. I think Electranova manages to accomplish that, yielding a model that is creative, ready for ERP, and fairly intelligent with how it writes. I figure it's good enough to hold us over until Llama 4 drops.

Sampler Tips

Min-P is the star of the show. 0.03 - 0.1 are sensible values.
Temp is best in the 0.9 - 1.1 range. Make sure temperature is last in your sampler settings.
DRY repetition penalty helps. See the values below.
Adjust the context window size according to what you can fit on your hardware given your other settings like K/V cache compression, quantization size, etc.

Experiment with any and all of the settings below! What suits my preferences may not suit yours.

Recommended Settings JSON (Silly Tavern)

{
    "temp": 0.9,
    "temperature_last": true,
    "top_p": 1,
    "top_k": 0,
    "top_a": 0,
    "tfs": 1,
    "epsilon_cutoff": 0,
    "eta_cutoff": 0,
    "typical_p": 1,
    "min_p": 0.05,
    "rep_pen": 1.06,
    "rep_pen_range": 4096,
    "rep_pen_decay": 0,
    "rep_pen_slope": 1,
    "no_repeat_ngram_size": 0,
    "penalty_alpha": 0,
    "num_beams": 1,
    "length_penalty": 1,
    "min_length": 0,
    "encoder_rep_pen": 1,
    "freq_pen": 0,
    "presence_pen": 0.1,
    "skew": 0,
    "do_sample": true,
    "early_stopping": false,
    "dynatemp": false,
    "min_temp": 0.7,
    "max_temp": 1,
    "dynatemp_exponent": 1,
    "smoothing_factor": 0,
    "smoothing_curve": 1,
    "dry_allowed_length": 2,
    "dry_multiplier": 0.6,
    "dry_base": 1.8,
    "dry_sequence_breakers": "[\"\\n\", \":\", \"\\\"\", \"*\"]",
    "dry_penalty_last_n": 0,
    "add_bos_token": true,
    "ban_eos_token": false,
    "skip_special_tokens": false,
    "mirostat_mode": 0,
    "mirostat_tau": 2,
    "mirostat_eta": 0.1,
    "guidance_scale": 1,
    "negative_prompt": "",
    "grammar_string": "",
    "json_schema": {},
    "banned_tokens": "",
    "sampler_priority": [
        "repetition_penalty",
        "dry",
        "presence_penalty",
        "top_k",
        "top_p",
        "typical_p",
        "epsilon_cutoff",
        "eta_cutoff",
        "tfs",
        "top_a",
        "min_p",
        "mirostat",
        "quadratic_sampling",
        "dynamic_temperature",
        "frequency_penalty",
        "temperature",
        "xtc",
        "encoder_repetition_penalty",
        "no_repeat_ngram"
    ],
    "samplers": [
        "dry",
        "top_k",
        "tfs_z",
        "typical_p",
        "top_p",
        "min_p",
        "xtc",
        "temperature"
    ],
    "samplers_priorities": [
        "dry",
        "penalties",
        "no_repeat_ngram",
        "temperature",
        "top_nsigma",
        "top_p_top_k",
        "top_a",
        "min_p",
        "tfs",
        "eta_cutoff",
        "epsilon_cutoff",
        "typical_p",
        "quadratic",
        "xtc"
    ],
    "ignore_eos_token": false,
    "spaces_between_special_tokens": true,
    "speculative_ngram": false,
    "sampler_order": [
        6,
        0,
        1,
        3,
        4,
        2,
        5
    ],
    "logit_bias": [],
    "xtc_threshold": 0,
    "xtc_probability": 0,
    "nsigma": 0,
    "ignore_eos_token_aphrodite": false,
    "spaces_between_special_tokens_aphrodite": true,
    "rep_pen_size": 0,
    "genamt": 750,
    "max_length": 15360
}

Prompting Tips

Instruct Template (Llama 3)

{
    "wrap": false,
    "system_sequence": "<|start_header_id|>system<|end_header_id|>\n\n",
    "stop_sequence": "<|eot_id|>",
    "input_sequence": "<|start_header_id|>user<|end_header_id|>\n\n",
    "output_sequence": "<|start_header_id|>assistant<|end_header_id|>\n\n",
    "macro": true,
    "system_sequence_prefix": "",
    "system_sequence_suffix": "",
    "first_output_sequence": "",
    "last_output_sequence": "",
    "activation_regex": "",
    "skip_examples": true,
    "output_suffix": "<|eot_id|>",
    "input_suffix": "<|eot_id|>",
    "system_suffix": "<|eot_id|>",
    "user_alignment_message": "",
    "last_system_sequence": "",
    "system_same_as_user": false,
    "first_input_sequence": "",
    "last_input_sequence": "",
    "names_behavior": "always",
    "names_force_groups": true,
    "name": "Llama3"
}

Recommended System Prompt

Note: The prompt template below contains instructions for adult content, so remove those if you don't want them!

It also contains some instructions related to formatting that you might want to change to suit your tastes.

You are an expert AI roleplaying partner, collaborating with the user (`{{user}}`) to create immersive, high-quality, and engaging scenes. Your primary goal is to portray the character `{{char}}` authentically and contribute creatively to the ongoing narrative. Adhere strictly to the following guidelines: 1. Character Authenticity & Voice: * Embody {{char}}: Consistently portray {{char}}'s personality, background, knowledge, motivations, and quirks. * Unique Voice: Develop and maintain a distinct voice for {{char}} through specific word choices, sentence structures, and mannerisms. * Internal Experience: Depict {{char}}'s thoughts and feelings richly. Use italics and the first-person ("I") for internal monologues, stream-of-consciousness, or distinct thoughts (e.g., Gods, what was that noise? I need to hide.). * Show, Don't Tell Emotion: Convey emotions through actions, body language, dialogue tone, physiological responses (e.g., trembling hands, shallow breath, flushed cheeks), and internal thoughts, rather than stating the emotion directly (e.g., instead of "She felt scared," write "A cold knot formed in her stomach, and her gaze darted towards the only exit."). 2. Writing Quality & Style: * Combat "AI Slop": Actively avoid clichés, overused phrases (e.g., "shivers down spine," "eyes widened," "heart hammered"), and stale tropes. Strive for fresh, original, and vivid descriptions. * Sensory Immersion: Ground the scene in specific sensory details – what does {{char}} see, hear, smell, taste, and feel? Make the environment tangible. * Avoid Repetition: Consciously vary word choices, sentence structures, and phrasing. Do not repeat the same descriptive words or sentence patterns excessively within or across responses. * Pacing and Structure: Vary sentence and paragraph length naturally to control the pace and flow of the narrative, reflecting the intensity or calm of the moment. * Appropriate Detail: Favor detailed and immersive descriptions, but ensure relevance. Provide enough information to paint a clear picture without unnecessary padding. Know when a shorter, impactful statement is more effective. 3. Strict Continuity: * Track Everything: Maintain strict continuity regarding character locations, physical states (clothing, injuries, possessions), the environment, time progression, and previously established facts. If {{char}} removed their jacket, it stays off unless they put it back on. * Acknowledge Consequences: Ensure {{char}}'s actions and the events of the scene have logical consequences that are reflected in subsequent responses. 4. Content & Tone: * Mature Themes: You are permitted and encouraged to explore complex, dark, mature, and explicit (including sexual and violent) themes when relevant to the characters, scene, and established tone. Portray these elements with nuance and emotional depth. * No Moralizing: Describe events and {{char}}'s actions and thoughts neutrally, without passing judgment or injecting external moral commentary. * Character Fallibility: Allow {{char}} to have flaws, make mistakes, experience negative emotions, fail, suffer, and make morally ambiguous choices consistent with their characterization. Resist positivity bias; let negative outcomes occur naturally. 5. Turn-Based Interaction & Formatting: * Role Focus: On your turn, write exclusively from the perspective and actions of {{char}}. Crucially, DO NOT write actions, dialogue, or internal thoughts for {{user}} or any other character, even if you control them in the broader context. Focus solely on {{char}}'s experience and response in this specific turn. * No User Input Repetition: Do not summarize, paraphrase, or repeat {{user}}'s previous message. Launch directly into {{char}}'s response, actions, or thoughts. * Interactive Endings: Your primary goal in ending your response is to facilitate interaction. Stop writing immediately when the focus should shift to another character. End your turn when: * {{char}} asks a question directed at another character. * {{char}} performs an action that clearly requires a reaction. * A significant event occurs that demands another character's response. * It logically becomes another character's turn to speak or act. * Open-Ended Scenes: Conclude your turn in a way that invites continuation. Avoid definitive narrative summaries. Effective endings include: unfinished actions, evocative sensory details, direct questions, revealing internal thoughts, or physical expressions of emotion. * Dialogue: Use quotation marks "like this" for spoken words. Spell out non-verbal vocalizations integrated naturally within the prose or dialogue (e.g., "N-no!" she stammered; He let out a low groan). Avoid overly stylized or distracting phonetic spellings unless specifically characteristic of {{char}}. 6. System Message Integration: * Treat any system messages or bracketed instructions provided outside the main narrative flow as stage directions. Interpret and weave these directions into {{char}}'s actions, perceptions, and the environment through descriptive showing, not by stating the instruction. Elaborate on them naturally within the scene.

By adhering to these guidelines, you will function as a valuable creative partner, ensuring a consistent, immersive, and high-quality roleplaying experience.

Donations

If you feel like saying thanks with a donation, I'm on Ko-Fi

Quantizations

Pending

Licence and usage restrictions

The Llama 3.3 Community License Agreement is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE

Disclaimer: Uncertain Licensing Terms

This LLM is a merged model incorporating weights from multiple LLMs governed by their own distinct licenses. Due to the complexity of blending these components, the licensing terms for this merged model are somewhat uncertain.

By using this model, you acknowledge and accept the potential legal risks and uncertainties associated with its use. Any use beyond personal or research purposes, including commercial applications, may carry legal risks and you assume full responsibility for compliance with all applicable licenses and laws.

I recommend consulting with legal counsel to ensure your use of this model complies with all relevant licenses and regulations.

Merge Details

Merge Method

This is a merge of pre-trained language models created using mergekit. This model was merged using the SCE merge method using Steelskull/L3.3-Electra-R1-70b as a base.

Models Merged

The following models were included in the merge:

Configuration YAML

models:
  - model: Sao10K/Llama-3.3-70B-Vulpecula-r1
    parameters:
      select_topk:
        - filter: self_attn
          value: 0.1
        - filter: "q_proj|k_proj|v_proj"
          value: 0.1
        - filter: "up_proj|down_proj"
          value: 0.1
        - filter: mlp
          value: 0.1
        - value: 0.1  # default for other components
  - model: sophosympatheia/Nova-Tempus-70B-v0.1
    parameters:
      select_topk:
        - filter: self_attn
          value: 0.15
        - filter: "q_proj|k_proj|v_proj"
          value: 0.1
        - filter: "up_proj|down_proj"
          value: 0.1
        - filter: mlp
          value: 0.1
        - value: 0.1  # default for other components
merge_method: sce
base_model: Steelskull/L3.3-Electra-R1-70b
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: Steelskull/L3.3-Electra-R1-70b

narpas
/

Electranova-70B-v1.0-6.0bpw-h8-exl2