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Tekimax
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Model Details
Model Provider
Tekimax
Model Tree
ibm-granite/granite-3.1-1b-a400m-instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Tekimax/granite-ml-coder")model = AutoModelForCausalLM.from_pretrained( "Tekimax/granite-ml-coder", dtype=torch.float32, attn_implementation="eager") messages = [ {"role": "system", "content": "You are an expert Python machine-learning engineer."}, {"role": "user", "content": "Write a scikit-learn pipeline to classify the iris dataset and explain how you avoid overfitting."},]enc = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True, enable_thinking=False)out = model.generate(**enc, max_new_tokens=400, do_sample=True, temperature=0.7, top_p=0.9)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))bash
ollama run tekimaxllc/granite-ml-coder "Write a Keras autoencoder for network-traffic anomaly detection"A 4-bit Q4_K_M build (~378 MB) is available at
Tekimax/granite-ml-coder-GGUF:
bash
llama-cli -m granite-ml-coder-Q4_K_M.gguf -p "Write a sklearn pipeline"| Base model | ibm-granite/granite-3.1-1b-a400m-instruct (Apache-2.0, IBM) — ~1.3B total / 400M active MoE |
| Data | iamtarun/python_code_instructions_18k_alpaca, filtered to ML/DS rows (≈2,341 examples) |
| Method | Full fine-tune, instruction format with the Granite chat template; loss computed on the assistant answer only (prompt tokens masked) |
| Schedule | 2 epochs · effective batch size 16 · LR 2e-5 cosine · max_len 512 |
| Hardware | Apple M2 Ultra, CPU (the MPS/Metal backend was unstable for fine-tuning on torch 2.12 — see the workshop appendix) |
| Result | training loss decreasing steadily, no NaN |
Apache-2.0, inherited from the base model and dataset.
transformers Trainer; quantized with llama.cpp.import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Tekimax/granite-ml-coder")model = AutoModelForCausalLM.from_pretrained( "Tekimax/granite-ml-coder", dtype=torch.float32, attn_implementation="eager") messages = [ {"role": "system", "content": "You are an expert Python machine-learning engineer."}, {"role": "user", "content": "Write a scikit-learn pipeline to classify the iris dataset and explain how you avoid overfitting."},]enc = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True, enable_thinking=False)out = model.generate(**enc, max_new_tokens=400, do_sample=True, temperature=0.7, top_p=0.9)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))