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

.gitattributes

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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`.

chat_template.jinja

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+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content'] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "Knowledge Cutoff Date: April 2024.
+Today's Date: " + strftime_now('%B %d, %Y') + ".
+You are Granite, developed by IBM." %}
+    {%- if tools and documents %}
+        {%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.
+
+Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+    {%- elif tools %}
+        {%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
+    {%- elif documents %}
+        {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+    {%- else %}
+        {%- set system_message = system_message + " You are a helpful AI assistant." %}    
+    {%- endif %}
+    {%- if 'citations' in controls and documents %}
+        {%- set system_message = system_message + '
+
+In your response, use the symbols <co> and </co> to indicate when a fact comes from a document in the search result, e.g <co>0</co> for a fact from document 0. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
+    {%- endif %}
+    {%- if 'hallucinations' in controls and documents %}
+        {%- set system_message = system_message + '
+
+Finally, after the response is written, include a numbered list of sentences from the response that are potentially hallucinated and not based in the documents.' %}
+    {%- endif %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>
+' }}
+{%- if tools %}
+    {{- '<|start_of_role|>tools<|end_of_role|>' }}
+    {{- tools | tojson(indent=4) }}
+    {{- '<|end_of_text|>
+' }}
+{%- endif %}
+{%- if documents %}
+    {{- '<|start_of_role|>documents<|end_of_role|>' }}
+    {%- for document in documents %}
+        {{- 'Document ' + loop.index0 | string + '
+' }}
+        {{- document['text'] }}
+        {%- if not loop.last %}
+            {{- '
+
+'}}
+        {%- endif%}
+    {%- endfor %}
+    {{- '<|end_of_text|>
+' }}
+{%- endif %}
+{%- for message in loop_messages %}
+    {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>
+' }}
+    {%- if loop.last and add_generation_prompt %}
+        {{- '<|start_of_role|>assistant' }}
+            {%- if controls %}
+                {{- ' ' + controls | tojson()}}
+            {%- endif %}
+        {{- '<|end_of_role|>' }}
+    {%- endif %}
+{%- endfor %}

config.json

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+{
+  "architectures": [
+    "GraniteMoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_multiplier": 0.015625,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "embedding_multiplier": 12.0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "logits_scaling": 6.0,
+  "max_position_embeddings": 131072,
+  "model_type": "granitemoe",
+  "num_attention_heads": 16,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "num_local_experts": 32,
+  "output_router_logits": false,
+  "pad_token_id": 0,
+  "residual_multiplier": 0.22,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1500000.0,
+    "rope_type": "default"
+  },
+  "router_aux_loss_coef": 0.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.9.0",
+  "use_cache": true,
+  "vocab_size": 49155
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": [
+    0
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "5.9.0"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|end_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 9223372036854775807,
+  "pad_token": "<|end_of_text|>",
+  "padding_side": "left",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<|end_of_text|>",
+  "vocab_size": 49152
+}

training_args.bin

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