GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
oddadmix
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
oddadmix
Model Tree
Base
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
| Parameters | ~51.8M |
| Architecture | Llama (LlamaForCausalLM) |
| Hidden size | 512 · Layers 12 · Heads 8 (GQA, 4 KV) · head_dim 64 |
| Intermediate size | 1408 |
| Vocab size | 32000 (custom Arabic byte-level BPE) |
| Context length | 2048 |
| Positional encoding | RoPE (θ = 10000) |
| Tied embeddings | yes |
| Type | Base / pretrained (no chat template) |
| License | Apache-2.0 |
kaust-generative-ai/fineweb-edu-ar
(config ar), streamed and tokenized to a memory-mapped token bin.ByteLevelBPETokenizer), vocab 32000,
specials <s> / </s> / <unk> / <pad>.The architecture and training loop follow SupraLabs/Supra-50M-Base
("Project Chimera — 50M Llama"). The pretraining script (train.py) is included in
this repository.
The tokenizer uses the TokenizersBackend class, which requires transformers>=5.12.
This is a base model — prompt it as a text completer, not a chat assistant:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "oddadmix/50M-2048-Emhotob"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)prompt = "اللغة العربية هي"ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=128, do_sample=True,temperature=0.8, top_p=0.9, repetition_penalty=1.2,)print(tok.decode(out[0], skip_special_tokens=True))
| Model | What it does |
|---|---|
oddadmix/Nawah-50M-Egyptian-18y-Persona | Speaks with an 18-year-old Egyptian persona |
oddadmix/Emhotob-50M-GPRO-Arabic-Final | Arabic tool-calling / function-calling (GRPO-tuned) |
Intended use. A from-scratch Arabic base model for research and as a fine-tuning starting point; a CPU-friendly baseline for tiny Arabic SLM experiments.
Limitations. As a base model it does not follow instructions or hold a
conversation out of the box — fine-tune it first. At ~50M parameters it is a
proof of concept: expect limited world knowledge, weak reasoning, and repetition
(use a repetition_penalty). Training data is web text (fineweb-edu-ar), so it
carries that corpus's biases. Not suitable for factual, medical, legal, or financial use.
SupraLabs/Supra-50M-Base — "Project Chimera" (Apache-2.0).kaust-generative-ai/fineweb-edu-ar.torch.compile