Create README.md

README.md

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+---
+language:
+- en
+license: other
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- python
+- code-generation
+- code-assistant
+- causal-lm
+- full-finetune
+- hunyuan
+- transformers
+- safetensors
+- instruct
+base_model:
+- tencent/Hunyuan-0.5B-Instruct
+model-index:
+- name: Hunyuan-PythonGOD-0.5B
+  results: []
+datasets:
+- WithinUsAI/Python_GOD_Coder_Omniforge_AI_12k
+- WithinUsAI/Python_GOD_Coder_5k
+- WithinUsAI/Legend_Python_CoderV.1
+---
+
+# Hunyuan-PythonGOD-0.5B
+
+Hunyuan-PythonGOD-0.5B is a Python-focused full fine-tune of `tencent/Hunyuan-0.5B-Instruct`, built for code generation, coding assistance, implementation tasks, and instruction-following for Python-heavy workflows.
+
+This release is intended as a compact coding model that keeps the small footprint of the 0.5B Hunyuan base while shifting its behavior toward practical Python generation and code-oriented responses.
+
+## Model Details
+
+### Model Description
+
+- **Model name:** `gss1147/Hunyuan-PythonGOD-0.5B`
+- **Base model:** `tencent/Hunyuan-0.5B-Instruct`
+- **Architecture:** causal decoder-only language model
+- **Model family tag:** `hunyuan_v1_dense`
+- **Primary domain:** Python coding / coding assistant
+- **Parameter count:** ~0.5B
+- **Weights format:** safetensors
+- **Tensor type in repo:** F16
+
+### Developed by
+
+- **Shared by:** `gss1147`
+
+### Finetuned from model
+
+- `tencent/Hunyuan-0.5B-Instruct`
+
+## Intended Uses
+
+### Direct Use
+
+This model is intended for:
+
+- Python function generation
+- Python script writing
+- debugging-oriented coding help
+- implementation tasks
+- code completion
+- coding chat assistants
+- lightweight local or cloud inference where a small coding model is preferred
+
+### Downstream Use
+
+Possible downstream uses include:
+
+- code copilots
+- coding bots
+- Python tutoring helpers
+- automation script generation
+- benchmark experimentation for small code LLMs
+
+### Out-of-Scope Use
+
+This model is not designed for:
+
+- safety-critical code deployment without human review
+- medical, legal, or financial decision support
+- secure production code without auditing
+- autonomous execution pipelines without sandboxing
+- guaranteed factual or bug-free code generation
+
+## Training Details
+
+### Training Objective
+
+This model was trained as a **full fine-tune**, not as an adapter-only release.
+
+Based on the training workflow you described and the run logs you shared, this release is meant to represent:
+
+- **full-parameter fine-tuning**
+- **no LoRA**
+- **no QLoRA**
+- **no PEFT adapters in the final model**
+- **standard exported Hugging Face model weights**
+
+### Training Data
+
+This model was trained on the following datasets:
+
+- `WithinUsAI/Python_GOD_Coder_Omniforge_AI_12k`
+- `WithinUsAI/Python_GOD_Coder_5k`
+- `WithinUsAI/Legend_Python_CoderV.1`
+
+From the training logs you shared, the combined training corpus used:
+
+- **11,760 rows** from `Python_GOD_Coder_Omniforge_AI_12k`
+- **5,000 rows** from `Python_GOD_Coder_5k`
+- **5,000 rows** from `Legend_Python_CoderV.1`
+
+**Total rows:** **21,760**
+
+### Training Procedure
+
+From the training setup you shared, this model was trained with:
+
+- **dual-GPU Kaggle training**
+- **DeepSpeed-assisted distributed training**
+- **full model fine-tuning**
+- **evaluation during training**
+- **final-save upload flow to Hugging Face**
+
+### Sequence Length
+
+- **Practical fine-tuning sequence length:** 4096 tokens
+
+### Context Window Note
+
+If the base model family exposes larger context metadata in config fields, that should not be taken as proof that the full fine-tuning run itself was performed at that larger length. This release should be treated as fine-tuned at **4096 tokens** unless revalidated separately.
+
+## Evaluation
+
+Formal benchmark results are not finalized in this card.
+
+Benchmark attempts were made on free public coding benchmarks such as:
+
+- HumanEval+
+- MBPP+
+- BigCodeBench-style workflows
+
+However, based on the evaluation runs you shared, the harness setup encountered tool/runtime issues during some benchmark attempts, so this card does **not** claim final official benchmark scores yet.
+
+### Observed Training Behavior
+
+From the run logs you shared during training, the model showed:
+
+- strong reduction in training loss over time
+- strong reduction in eval loss over time
+- stable continued learning well into the run
+- increasingly code-specialized behavior relative to the base model
+
+Examples from your shared eval progression included values around:
+
+- ~0.2879 early in training
+- ~0.1071
+- ~0.0604
+- ~0.0550
+- ~0.0422
+- ~0.0329
+- ~0.0266
+- ~0.0299
+- ~0.0290
+
+These are training/eval-run observations, not official public benchmark scores.
+
+## How to Use
+
+### Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "gss1147/Hunyuan-PythonGOD-0.5B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "Write a Python function that merges overlapping intervals."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=False,
+    )
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))