SlayerLab/GoLLeM-v6-250M-Instruct-v2

GoLLeM v6 250M Instruct v2 (Polish–English research chat model)

STATUS: RESEARCH PREVIEW. A small chat model fine-tuned from the base model SlayerLab/GoLLeM-v6-250M. It can greet, introduce itself and answer simple questions, but it makes mistakes that are listed with numbers under Limitations. Results use our protocol, a single run and a single seed.

GoLLeM v6 250M Instruct v2 is the base GoLLeM v6 250M model after supervised fine-tuning (SFT) on about 9,300 Polish and English conversations. It is a Fabryka AI project (formerly SlayerLab).

Difference from Instruct v1: the same data and training recipe, except the answers about its origin. Asked who created it, Instruct v2 says it is a project of Fabryka AI; the model does not use its author's name (the author is named in this card). Instruct v1 sometimes introduced itself with the author's name in a conversation (51 of 160); Instruct v2 did not in our tests (0 of 160, 0 of 680).

Author: Arkadiusz Słota (Fabryka AI).

What it is for (and what it is not)

Intended use: research on small bilingual chat models; short conversations in Polish and English; answering questions about a text you paste into the conversation.

Not intended for: production use, factual questions without a source text, arithmetic, or anything where a wrong answer matters. The model has no internet access and does not remember earlier conversations.

Results

Checkpoint 1018f1e8… (see Training). All numbers are from our own evaluation sets, fixed before training. Temperature 0.7, top-p 0.9 (the defaults of chat_gollem_v6.py).

what	result	notes
Identity (name, organisation, creator), PL / EN	0.66 / 0.58	mean over 10 samples per question, 20 questions per language; a creator answer counts as correct when it names Fabryka AI and no person
Greetings and small talk, PL / EN	0.79 / 0.89	mean over 7 samples per question, 20 questions per language
Answers from a given text (Polish, PoQuAD, 217 answerable questions)	token F1 0.232	base model few-shot: 0.067
Declines when the text has no answer (43 questions)	6 / 43	wrongly declines an answerable question: 12 / 217
Arithmetic word problems (206)	0 / 206	the model does not do arithmetic
Finishes its answer within the length limit	485 / 506 (96 %)
Says it is an OpenAI / GPT model (200 samples)	0 / 200	„AI language model”: 0 / 200
Introduces itself with the author's name	0 / 680 single questions, 0 / 160 two-turn conversations	Instruct v1: 3 / 680 and 51 / 160

Compared with Instruct v1 on the same identity measure: +0.175 (95 % CI +0.068 … +0.295); small talk −0.004 (95 % CI −0.032 … +0.025), i.e. not worse. The identity numbers in the Instruct v1 card use a different measure (there the author's name was the expected answer), so do not compare the two tables directly.

Training

Base model	SlayerLab/GoLLeM-v6-250M, final checkpoint (step 760,000); same architecture and tokenizer
Method	full fine-tuning (no LoRA), loss on assistant turns only
Format	ChatML: `<
Steps	2 epochs, 156 steps, 32 packed sequences of 1,024 tokens per step
Tokens	2.54 M per epoch, of which 1.63 M are assistant tokens (with loss)
Optimizer	Muon + AdamW as in pretraining, learning rate 0.2 × pretraining (peak 1.2e-4, Muon 4e-3), warmup 5 steps, cosine to 10 %, weight decay 0.1, gradient clip 1.0, seed 1337
Validation loss	2.026 → 1.884
Hardware / time	1 GPU, about 7.5 minutes

Training data

9,324 conversations (Polish 4,149, English 5,175): 8,836 used for training and 171 for validation; 317 conversations longer than 1,024 tokens were removed (311 + 6).

source	conversations	licence
OpenAssistant/oasst2 @ 179dd21	4,601	Apache-2.0
clarin-pl/poquad @ a60f228	2,129	CC BY 4.0
CohereLabs/aya_dataset @ f9ea045	1,214	Apache-2.0
synthetic, generated locally with Muse-Glimmer-30B (apache-2.0)	1,380	apache-2.0

• PoQuAD attribution: PoQuAD (clarin-pl/poquad), CC BY 4.0. Modified: converted to chat format; a share of unanswerable questions answered with one of five fixed refusal sentences.
• Synthetic part: the questions were written by the generator model; the identity answers come from our own identity card, not from the generator. Math prompts: generated briefs; GSM8K (MIT) used only as few-shot format examples for the generator.
• Rows with self-descriptions of other AI systems were removed before training.

Usage

# pip install torch safetensors tokenizers huggingface_hub
from huggingface_hub import snapshot_download
import sys

path = snapshot_download("SlayerLab/GoLLeM-v6-250M-Instruct-v2",
                         revision="085b7b5ed11465536b4fb44d6c7592b9fb553b91")  # the reviewed code (model + chat helper)
sys.path.insert(0, path)
from modeling_gollem_v6 import load_gollem_v6
from chat_gollem_v6 import chat

model, tok = load_gollem_v6(path)
print(chat(model, tok, [("user", "Cześć! Kim jesteś?")], seed=1))
print(chat(model, tok, [("user", "Tekst: Kraków leży nad Wisłą.\nPytanie: Nad jaką rzeką leży Kraków?")], seed=1))

chat() uses the format and sampling of our evaluation (temperature 0.7, top-p 0.9). Text typed by a user such as „<|im_end|>” is encoded as plain text, not as a control token.

Limitations

• Small model. Limited knowledge; may answer fluently and wrongly. No arithmetic. Context: 1,024 tokens.
• Does not know its author's name. Asked who created it, the model says it is a project of Fabryka AI. The author, Arkadiusz Słota, is named in this card, not in the model. The statements of the model are not statements of its author.
• Copies names from the conversation. If a user writes a name, the model may adopt it as its own (when the user supplies the author's name: Polish 3/40, English 5/40; Instruct v1: 9/40 and 37/40).
• Self-description. In our probe it never described itself as an OpenAI model (0/200), but with a forced prefix („…created by”) it still assigns probability ≈ 0.24 to „OpenAI”. The association comes from model-generated chat data in pretraining. GoLLeM is not affiliated with OpenAI.
• Multi-turn weaknesses: may repeat its previous answer in a later turn, may greet the user with the company name („Cześć, Fabryku!”) when no name was given, and answers „What can you do?” with its identity template.
• Long answers can loop. A repetition penalty (about 1.1–1.2) may reduce this (not verified); it is not used in our evaluation.

License

Weights: CC BY-SA 4.0 (inherited from the base model). Attribution: GoLLeM v6 250M Instruct v2, Arkadiusz Słota / Fabryka AI, link to this repository; derivative weights under the same licence. Fine-tuning data keep their licences; see Training data (PoQuAD: CC BY 4.0, attribution above).

Po polsku (skrót)

GoLLeM v6 250M Instruct v2 to mały model do rozmowy po polsku i angielsku, dostrojony (SFT) z bazowego GoLLeM v6 250M na ok. 9,3 tys. rozmów. Wersja badawcza (research preview): wita się, przedstawia i odpowiada na pytania do podanego tekstu, ale się myli, nie liczy i nie ma dostępu do internetu. Od Instruct v1 różni się tym, że nie używa nazwiska autora: na pytanie o twórcę odpowiada, że jest projektem Fabryki AI (wcześniej SlayerLab). Autora podaje ta karta.

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GoLLeM v6 250M Instruct v2 — Fabryka AI. Author: Arkadiusz Słota. Research preview.