GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
izambasiron
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
izambasiron
Model Tree
unsloth/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
| Base model | unsloth/gemma-4-E2B-it |
| Parameters | 2B |
| Fine-tuning | QLoRA (4-bit), rank 16 |
| Training data | 168 curated examples |
| Eval data | 42 held-out examples |
| Epochs | 3 |
| Final val loss | 0.546 |
| Precision | bfloat16 |
| VRAM (training) | ~6-8 GB (A100) |
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "izambasiron/gemma4-2b-markdown-review", torch_dtype=torch.bfloat16, device_map="auto")tokenizer = AutoTokenizer.from_pretrained("izambasiron/gemma4-2b-markdown-review") messages = [ {"role": "system", "content": "You are a Markdown document reviewer..."}, {"role": "user", "content": "# My Doc\n\n"}]inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)outputs = model.generate(inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))Download gemma-4-e2b-it.Q4_K_M.gguf and use with the included Modelfile:
bash
ollama create gemma4-markdown-review -f Modelfileollama run gemma4-markdown-reviewOr directly with llama.cpp:
bash
llama-cli -m gemma-4-e2b-it.Q4_K_M.gguf -p "<system prompt>" -f input.mdFine-tuned with Unsloth using the notebook at colab_finetune_gemma4_clean.ipynb.
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "izambasiron/gemma4-2b-markdown-review", torch_dtype=torch.bfloat16, device_map="auto")tokenizer = AutoTokenizer.from_pretrained("izambasiron/gemma4-2b-markdown-review") messages = [ {"role": "system", "content": "You are a Markdown document reviewer..."}, {"role": "user", "content": "# My Doc\n\n"}]inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)outputs = model.generate(inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))ollama create gemma4-markdown-review -f Modelfileollama run gemma4-markdown-reviewllama-cli -m gemma-4-e2b-it.Q4_K_M.gguf -p "<system prompt>" -f input.md