About Anis
Anis is an 8B parameter language model fine-tuned from Qwen2.5-7B using Supervised Fine-Tuning (SFT). It is the first stage in ATTLAB's open-source RLHF alignment pipeline, trained on At-Tawheed/Anis-RLHF — a curated 57.9 GB dataset of 33 instruction, preference, math, code, and multilingual subsets.
unsloth/qwen2.5-7b-unsloth-bnb-4bit (base)
└── Anis (SFT ← this model)
└── attlab-7b-dpo-v1 (DPO)
System prompt:
You are ATTLAB, a helpful, harmless, and honest AI assistant developed by the ATTLAB team.
Training Data — Anis-RLHF (57.9 GB · 33 subsets)
Table with columns: Category, Subsets| Category | Subsets |
|---|
| Instruction / Chat | openhermes_2_5, slim_orca, openorca_full, ultrachat_200k, smoltalk_1m, lmsys_chat_1m, tulu3_sft_mixture, oasst1_top_ranked |
| Preference / DPO | ultrafeedback, ultrafeedback_binarized, hh_rlhf_full, capybara_dpo_7k, helpsteer2 |
| Math / Reasoning | metamath_qa, numina_math_cot, openmath_instruct2, magpie_reasoning_250k |
| Code | opencode_instruct_5m, codefeedback_66k, evol_codealpaca_110k, magicoder_oss_75k |
| Synthetic | magpie_llama3_1m, magpie_llama31_1m, magpie_llama33_1m, magpie_qwen25_1m |
| Knowledge | wikipedia_en, gutenberg_books, stackexchange_qa, fineweb_edu |
| Multilingual | aya_multilingual, wikipedia_yoruba |
| WizardLM | wizardlm_evol_v2 |
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "At-Tawheed/Anis"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are ATTLAB, a helpful, harmless, and honest AI assistant developed by the ATTLAB team."},
{"role": "user", "content": "What is reinforcement learning from human feedback?"}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
outputs = model.generate(
input_ids,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
With Unsloth
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="At-Tawheed/Anis",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
Limitations
- SFT only: Anis is not fully aligned. For preference-optimized outputs use the DPO variant (
attlab-7b-dpo-v1).
- Hallucination: May produce factually incorrect outputs — do not use as a sole source of truth.
- Bias: Training data is sourced from the internet and inherits its biases.
Citation
@misc{anis2025,
author = {Ibraheem, Olushola Taoheed},
title = {Anis: A Supervised Fine-Tuned Language Model},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/At-Tawheed/Anis}},
note = {Attawheed AI Lab (ATTLAB). Fine-tuned from Qwen2.5-7B with Unsloth and TRL.}
}
ATTLAB · Hugging Face · GitHub · Dataset
