初始化项目，由ModelHub XC社区提供模型

Model: Tekimax/granite-ml-coder
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: ibm-granite/granite-3.1-1b-a400m-instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - code
+  - machine-learning
+  - data-science
+  - scikit-learn
+  - fine-tuned
+  - granite
+  - mlx
+  - gguf
+datasets:
+  - iamtarun/python_code_instructions_18k_alpaca
+language:
+  - en
+---
+
+# granite-ml-coder
+
+A compact **Python / machine-learning coding assistant**, fine-tuned from
+[`ibm-granite/granite-3.1-1b-a400m-instruct`](https://huggingface.co/ibm-granite/granite-3.1-1b-a400m-instruct). It writes runnable
+scikit-learn / pandas / NumPy code, explains ML pipeline steps, and reasons about
+everyday concepts like overfitting, cross-validation, and gradient descent.
+
+It is small enough to run **fully locally** — on a laptop CPU, via Ollama, or
+quantized to GGUF — which makes it a good private copilot for data-science work
+where your data shouldn't leave the machine.
+
+> Built as the reference model for the **TEKIMAX ML Model Workshop**
+> (fine-tune → host on HF/Ollama → build a production DL app).
+
+## Quick start
+
+### Transformers
+```python
+import torch
+from 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))
+```
+
+### Ollama (recommended for local use)
+```bash
+ollama run tekimaxllc/granite-ml-coder "Write a Keras autoencoder for network-traffic anomaly detection"
+```
+
+### GGUF / llama.cpp
+A 4-bit `Q4_K_M` build (~378 MB) is available at
+[`Tekimax/granite-ml-coder-GGUF`](https://huggingface.co/Tekimax/granite-ml-coder-GGUF):
+```bash
+llama-cli -m granite-ml-coder-Q4_K_M.gguf -p "Write a sklearn pipeline"
+```
+
+## Intended use
+
+- Drafting Python ML code (scikit-learn, pandas, NumPy, Keras) inside notebooks/IDEs
+- Explaining ML pipeline steps and concepts (overfitting, gradient descent, model choice)
+- A private, offline coding copilot for data-science tasks
+
+## Training
+
+| | |
+|---|---|
+| 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`](https://huggingface.co/datasets/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 |
+
+## Limitations
+
+- **It's a 1B model.** It learns *style, format, and common patterns* well, but
+  is not a frontier model — it can produce incomplete or subtly wrong code, and
+  it won't reliably "pick the best model" for hard problems. Treat its output as
+  a fast first draft and verify before use.
+- English, Python-focused. Strongest on classic ML (sklearn/pandas); weaker on
+  large, novel, or multi-file tasks.
+- Inherits any biases/limitations of the base model and the training dataset.
+
+## License
+
+Apache-2.0, inherited from the base model and dataset.
+
+## Citation / credits
+
+- Base model: [Granite](https://huggingface.co/ibm-granite/granite-3.1-1b-a400m-instruct)
+- Dataset: [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
+- Built with Hugging Face `transformers` `Trainer`; quantized with `llama.cpp`.