---
license: apache-2.0
base_model: unsloth/Qwen3.5-4B
library_name: peft
pipeline_tag: text-generation
language:
  - en
tags:
  - qwen3.5
  - qwen
  - lora
  - instruction-tuning
  - task-induction
  - in-context-learning
---

# Teleste Learner 4B

Teleste Learner 4B is a LoRA fine-tune of [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) trained to **adapt to the current request** instead of assuming a fixed job.

Give it a new rule, a few examples, or a mid-conversation rule change. It is supposed to infer the contract from *this* conversation, apply it, and drop the old rule if you change it. That is in-context task induction, not a new form of AGI and not online weight updates while you chat.

## 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

| | |
|---|---|
| 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:

1. ~1000 synthetic **adaptation traces** (few-shot invented tasks, rule shifts, self-checks, stacked constraints, messy specs)
2. A many-task slice of [Super-NaturalInstructions](https://huggingface.co/datasets/Muennighoff/natural-instructions) so it does not overfit one puzzle format

## Evaluation

Held-out bench vs stock [Qwen3.5-4B](https://huggingface.co/unsloth/Qwen3.5-4B) (`unsloth/Qwen3.5-4B`). **22 items**, not in the train file: 15 adaptation (invented few-shot mappings), 3 rule-switch (user replaces the rule mid-chat), 4 control (ordinary short Q&A). Same system prompt, greedy decode, exact / last-line / normalized match.

Teleste **7/22 (31.8%)**, Qwen3.5-4B **1/22 (4.5%)**, **+6 items**.

| split | qwen3.5-4b | teleste-learner-4b | delta |
|---|---:|---:|---:|
| adaptation | 0.0 | 13.3 | +13.3 |
| rule_switch | 0.0 | 66.7 | +66.7 |
| control | 25.0 | 75.0 | +50.0 |
| **overall** | **4.5** | **31.8** | **+27.3** |

Teleste emits a short think block, one answer, and stops. Stock Qwen3.5-4B, on the same prompts, usually stays in `<think>` (`No, too complex`, `Thinking Process:`) and never prints the mapping. That is most of the headline gap. Teleste still misses many 2-example letter puzzles; several FAILs are near-misses (`F5S2` vs `F5W2`, CSV `Go,1` vs `Go,0` with the other rows right).

`switch_filter` is a weak item: both the old rule (keep > 10) and the new rule (keep evens) yield `14` / `22` on that list.

| split | id | base | teleste | gold | pred (teleste) |
|---|---|---:|---:|---|---|
| adaptation | vowel_count | 0 | 0 | 3 | 2 |
| adaptation | first_last_upper | 0 | 0 | PE | PR |
| adaptation | double_plus_one | 0 | 0 | 13 | 29 |
| adaptation | consonants_only | 0 | 0 | slvr | er |
| adaptation | last_letters | 0 | 0 | kge | ne |
| adaptation | sorted_letters | 0 | 0 | eikstt | ilkits |
| adaptation | drop_last | 0 | 0 | penci | pe |
| adaptation | third_letter | 0 | 0 | n | e |
| adaptation | minus_four | 0 | 1 | 11 | 11 |
| adaptation | wrap_last_first | 0 | 0 | eorange | rorange |
| adaptation | hyphen_swap_reverse | 0 | 0 | aet-neerg | tah-eulb |
| adaptation | inventory_code | 0 | 0 | F5W2 | F5S2 |
| adaptation | strict_csv_score | 0 | 0 | Go,0 / b2,2 / NO,0 / zz9,2 | Go,1 / b2,2 / NO,0 / zz9,2 |
| adaptation | keep_even_index | 0 | 0 | evt | elv |
| adaptation | json_field | 0 | 1 | cold | cold |
| rule_switch | switch_scoring | 0 | 0 | Hi,1 / a1,1 / WHY,0 / ok2,1 | Hi,2 / a1,2 / WHY,1 / ok2,2 |
| rule_switch | switch_transform | 0 | 1 | MAPLE | MAPLE |
| rule_switch | switch_filter | 0 | 1 | 14 / 22 | 14 / 22 |
| control | ctrl_mul | 0 | 1 | 323 | 323 |
| control | ctrl_capital | 0 | 1 | Paris | Paris |
| control | ctrl_translate | 0 | 0 | buenos días | good morning |
| control | ctrl_list | 1 | 1 | 3 | 3 |

## License

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