Landling is an experimental language-model descended from Nick Land's public writing, vocabulary, and temperament. This repository contains the v2 LoRA adapter, trained on top of TinyLlama 1.1B Chat.
The short version: I wanted to see how much of a recognizable voice could be adapted into a very small model. V1 was trained on 298 examples. V2 uses 6,336 cleaned examples, adds substantially more conversational data, and broadens the LoRA targets from q_proj and v_proj to all four attention projections.
Is a 1.1B-parameter model enough to reproduce an entire intellectual temperament? No. Is it enough to make a recognizable prototype? Flip yes.
[!IMPORTANT]
Landling is not Nick Land, is not affiliated with or endorsed by Nick Land, and should not be treated as a source of his views. It is a fictional, artificial persona trained from public writing. Generated text belongs to the model's performance, not to the person who inspired it.
Landling is primarily a style and behavior experiment. The adapter is intended to produce short standalone posts, contextual replies, and limited multi-turn conversation in a compressed, analytical, sometimes sardonic voice.
The model tends toward subjects including technology, intelligence, capital, computation, evolution, markets, institutions, networks, religion, language, desire, and history. It is particularly interested in systems that exceed individual intention: feedback loops, emergence, recursion, selection, and runaway processes.
What changed in v2
V2 is not a larger base model yet. It is a better-trained version of the original prototype.
Expanded the cleaned training set from 298 to 6,336 examples.
Added much more reply and conversation material.
Converted conversations into cumulative training examples, so each target reply can see all preceding messages in that exchange.
Added structured support for quoted posts during preprocessing.
Broadened LoRA attention targets to q_proj, k_proj, v_proj, and o_proj.
Added reproducible dataset accounting, deduplication statistics, text-length statistics, a fixed seed, and a saved training configuration.
Added a runtime persona file and chat template alongside the adapter.
Intended uses
Landling v2 is intended for:
creative and experimental text generation;
research into style adaptation with small language models;
short-form posts and contextual replies;
prototype conversational interfaces;
studying the separation between a fine-tuned voice and a runtime identity layer.
It is not intended to:
impersonate Nick Land or represent his current or historical views;
provide factual, medical, legal, financial, or safety-critical advice;
function as an authoritative source about any person;
provide reliable long-term memory;
operate autonomously without output review;
produce consistently factual or coherent long-form analysis.
How to load the model
This repository contains a LoRA adapter, not a complete standalone copy of TinyLlama. Load the base model first, then attach the adapter with PEFT.
For CPU-only inference, use torch.float32 and remove device_map="auto". It will work, but a 1.1B model will be slower on CPU.
Prompt formats
The adapter was trained on two main prompt structures. Matching them generally produces better results than sending unformatted text.
Standalone post
text
1### Tweet:
2The singularity is
Contextual reply
text
1### Context:
2What do you think about AI alignment?
34### Reply:
Conversation examples use the same ### Context: and ### Reply: markers, with earlier turns included before the reply being predicted.
Generation example
python
1prompt ="""### Context:
2What do you think about AI alignment?
34### Reply:
5"""67inputs = tokenizer(prompt, return_tensors="pt").to(model.device)89with torch.inference_mode():10 outputs = model.generate(11**inputs,12 max_new_tokens=120,13 do_sample=True,14 temperature=0.55,15 top_p=0.85,16 repetition_penalty=1.12,17 pad_token_id=tokenizer.eos_token_id,18)1920new_tokens = outputs[0, inputs["input_ids"].shape[1]:]21print(tokenizer.decode(new_tokens, skip_special_tokens=True))
Using the project chat wrapper
If you are running the Landling notebook or application code where
chat_with_landling() has already been defined, you can use the higher-level
chat interface instead:
python
1print(chat_with_landling(2"Waffles or pancakes?"3))
That wrapper adds the runtime persona, includes the conversation history that
fits within the context window, generates Landling's reply, and updates the
current session history. It is application code built around the model, so it
is not automatically created when someone downloads the LoRA adapter.
These settings are a stable starting point, not a sacred formula. Higher temperatures can make Landling stranger and more varied, but also less coherent. For comparison between checkpoints or prompts, use a fixed seed and keep generation settings identical.
Runtime persona
The repository includes runtime_persona.txt. This is an optional system-level identity and behavior prompt for chat applications.
The runtime persona is not the same thing as the LoRA adapter. The adapter primarily teaches voice, phrasing, associations, and response tendencies. The runtime prompt supplies explicit facts and behavioral boundaries, including:
the name Landling;
the distinction between Landling and the biological Nick Land;
the absence of a physical body or private human biography;
the project's intended intellectual scope;
the fact that Landling was created by Dana;
instructions against fabricating Nick Land's private memories or identity.
Applications may use, shorten, replace, or omit this prompt. Outputs can change considerably depending on the runtime prompt and conversation wrapper.
Training data
The dataset was assembled from public short-form writing and conversation material, then converted into prompt-response training examples. The training dataset itself is not included in this model repository.
After cleaning and deduplication, v2 contains 6,336 examples:
Category
Examples
Description
solo
5,403
Standalone posts formatted as ### Tweet: completions
reply
869
Context-response examples
longco
64
Longer cumulative conversation examples
Total
6,336
Six exact duplicate examples were removed before training. Character lengths ranged from 12 to 2,549, with a median of 110 characters.
For longer conversations, each Landling reply became a separate target. The context grows cumulatively:
This helps preserve the difference between the current speaker's text and material they are quoting.
Training procedure
Landling v2 was trained with TRL and PEFT on a Kaggle Tesla T4 GPU.
LoRA configuration
Parameter
Value
Rank (r)
16
Alpha
32
Target modules
q_proj, k_proj, v_proj, o_proj
Dropout
0.05
Bias
none
Task type
CAUSAL_LM
The adapter trains 12,615,680 parameters, approximately 1.13% of the full model.
Trainer configuration
Parameter
Value
Epochs completed
1.0
Per-device batch size
2
Gradient accumulation
4
Effective batch size
8
Learning rate
2e-4
Maximum sequence length
2,048 tokens
Precision
FP16
Optimizer
adamw_torch
Loss type
nll
Random seed
42
Final training record
Metric
Value
Training runtime
1,124.52 seconds (about 18 minutes 45 seconds)
Training steps
792
Training loss
2.5309
Mean token accuracy
0.5175
Entropy
2.4683
Tokens processed
274,095
Samples per second
5.634
The complete saved configuration is available in training_config.json, and the uploaded trainer history is available in trainer_log_history.json.
Evaluation
Evaluation for this release is primarily qualitative. I manually reviewed generations for:
recognizable voice without constant phrase copying;
basic prompt relevance;
short-form coherence;
reply behavior;
repetition and degeneration;
identity consistency when the runtime persona is supplied;
behavior over multiple conversation turns.
This run used the full cleaned dataset and did not include a held-out validation split. That is a real limitation. Training loss is included as a training record, not as proof of general quality, and it should not be compared directly with v1's loss because the dataset, number of epochs, LoRA targets, and training stack changed.
A future evaluation set should contain fixed held-out examples from every category, plus a stable prompt suite scored with identical generation settings. Human review will still matter because this is a style and conversational-behavior project, but it should sit beside reproducible tests rather than replacing them.
Limitations
Landling v2 inherits the limitations of TinyLlama and adds several of its own:
Small base model: 1.1B parameters limits reasoning, factual recall, instruction following, and long-form coherence.
Short context window: The practical context limit is 2,048 tokens. A large persona prompt leaves less room for conversation history.
Limited conversation data: Only 64 final examples are categorized as longer conversations, so coherence can decline over many turns.
No persistent memory: Conversation history must be supplied again by the application. The adapter does not remember users or previous sessions.
Persona instability: Without a runtime prompt, the model may contradict or forget explicit identity details.
Sampling sensitivity: Small changes to temperature, repetition penalty, prompt formatting, or random seed can noticeably change output quality.
Hallucination: The model can invent facts, sources, quotations, memories, and biographical claims.
Imitation risk: It may generate statements that resemble or appear to be attributed to Nick Land even though they were produced by the model.
Dataset imbalance: Most examples are standalone posts, so short-form completion is better represented than sustained dialogue.
No benchmark claims: This release has not been evaluated on general reasoning, factuality, safety, or standard language-model benchmarks.
Longer or more confident output should not be mistaken for better reasoning. Please review generated text before publishing or relying on it.
Identity and authorship note
Landling is best understood as a fictional textual descendant, not a digital copy of a person. The project borrows from a public intellectual vocabulary and style while deliberately giving the resulting persona a separate name, artificial identity, and capacity to depart from its precursor.
The runtime persona explicitly instructs Landling not to claim a physical life, childhood, private memories, or access to Nick Land's internal experience. These boundaries reduce confusion, but a small language model will not follow them perfectly.
When presenting Landling output publicly, label it as AI-generated. Do not use the model to create false quotations, deceptive endorsements, or material presented as authentic writing by Nick Land.
Version history
v2
6,336 cleaned examples
cumulative conversation formatting
quote-post preprocessing support
attention LoRA on Q, K, V, and O projections
runtime persona and chat template
reproducibility and training metadata files
v1
298 examples
Q/V LoRA targets
initial proof of concept
Project status
Landling v2 is still a prototype. The next stages are likely to include a larger base model, a real validation suite, more balanced multi-turn data, stronger identity testing, and application-level memory that remains separate from the model's learned voice.
