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At-Tawheed
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At-Tawheed
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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.
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unsloth/qwen2.5-7b-unsloth-bnb-4bit (base) └── Anis (SFT ← this model) └── attlab-7b-dpo-v1 (DPO)System prompt:
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You are ATTLAB, a helpful, harmless, and honest AI assistant developed by the ATTLAB team.| 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 |
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport 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))python
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)attlab-7b-dpo-v1).bibtex
@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
unsloth/qwen2.5-7b-unsloth-bnb-4bit (base) └── Anis (SFT ← this model) └── attlab-7b-dpo-v1 (DPO)from transformers import AutoTokenizer, AutoModelForCausalLMimport 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))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)@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.}}