Davitotty1

Teleste-Learner-4B

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README

License: apache-2.0

What it is good at

Invented mappings shown with a few labeled examples
Following a procedure you just defined (format, cipher, filter, schema)
Switching behavior when a later message replaces the rule
Staying quiet on extra commentary when the contract is strict

What it is not

Not a general agent with memory across sessions
Not trained as a specialist in one domain (medicine, law, a single company's docs)
Not guaranteed to invent the correct rule when the examples are ambiguous

How to use

Load the repo with Transformers. If this repo is a merged 16-bit model:

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "Davitotty1/Teleste-Learner-4B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, dtype=torch.float16, device_map="auto"
)

system = (
    "You adapt to the current request. Infer the user's goal, the hidden rules, "
    "and the output contract from this conversation only. If examples are present, "
    "the mapping in those examples is the law. If a later message changes the rules, "
    "the new rules replace the old ones. Check the answer against the inferred "
    "contract before you finish. Do not keep a default job."
)
messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "examples: walrus→12, turtle→12, pig→6. now sloth → ?"},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, temperature=0.6, top_p=0.95, top_k=20)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

If the repo is LoRA-only, load the base model first and attach the adapter:

python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "unsloth/Qwen3.5-4B"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, "Davitotty1/Teleste-Learner-4B")

Needs a recent transformers with Qwen3.5 support (v5+).

Sampling that works well: temperature=0.6, top_p=0.95, top_k=20. Allow enough max_new_tokens for a short <think> block plus the answer.

Training

Table

Base	Qwen/Qwen3.5-4B (Unsloth image `unsloth/Qwen3.5-4B`)
Method	16-bit LoRA (rank 16, alpha 16), not 4-bit QLoRA
Targets	q, k, v, o, gate, up, down
Context	2048
Hardware	Kaggle Tesla T4, Unsloth LoRA (Qwen3.5 uses float32 training on T4)
Objective	Supervised chat SFT

Data mix:

~1000 synthetic adaptation traces (few-shot invented tasks, rule shifts, self-checks, stacked constraints, messy specs)
A many-task slice of Super-NaturalInstructions so it does not overfit one puzzle format

License

Apache 2.0, inherited from Qwen3.5-4B.

Available on FriendliAI

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Model Details

Model Provider

Davitotty1

Model Tree

Base

unsloth/Qwen3.5-4B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality