Alpha-Coder-14B

Alpha-Coder-14B is a fine-tuned version of Qwen2.5-Coder-14B-Instruct, adapted via LoRA to produce typed, tested Python code. This repo contains both the fused fp16 weights and a Q4_K_M GGUF quant for local inference (e.g. with Ollama or llama.cpp).

Base model attribution

This model is a derivative of Qwen/Qwen2.5-Coder-14B-Instruct, released by the Qwen team under the Apache 2.0 license. Alpha-Coder-14B is redistributed under the same license, as permitted by Apache 2.0 for derivative/renamed works, with attribution to the original model and authors.

• Base model: Qwen/Qwen2.5-Coder-14B-Instruct
• License: Apache 2.0

Training details

• Method: LoRA fine-tuning
• Hardware: Apple Silicon M5, 24GB unified memory
• Framework: MLX (4-bit base model during training)
• LoRA config: rank = 64, alpha = 128, learning rate = 2e-6
• Steps: 6,160
• Validation loss: 0.383 → 0.252
• Post-training: LoRA adapter fused into the base model, dequantized to fp16 HF safetensors, then converted and quantized to GGUF (Q4_K_M, 8.4GB) via llama.cpp

Benchmarks

Benchmark	Base (Qwen2.5-Coder-14B-Instruct)	Alpha-Coder-14B
MMLU	72%	77%
GSM8K	~93% (no regression)	93%

No measurable forgetting was observed on GSM8K after fine-tuning, while MMLU improved by 5 points.

Files in this repo

File	Description
*.safetensors	Fused fp16 weights (LoRA merged into base), full precision
tokenizer* / *.json	Tokenizer and config files
alpha-14b-Q4_K_M.gguf	Q4_K_M quantized GGUF, ~8.4GB, for llama.cpp / Ollama

Usage with Ollama

1. Download alpha-14b-Q4_K_M.gguf from this repo.
2. Create a Modelfile in the same directory (use your actual system prompt from your local Modelfile).
3. Build and run:

ollama create alpha-coder -f Modelfile
ollama run alpha-coder

Usage with llama.cpp

./llama-cli -m alpha-14b-Q4_K_M.gguf -p "Write a Python function that ..."

Usage with transformers (fp16 safetensors)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Jay2003Bhatt/alpha-coder-14b", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Jay2003Bhatt/alpha-coder-14b")

Intended use

Alpha-Coder-14B is intended as a coding assistant producing typed, tested Python code. As with any fine-tuned model, evaluate outputs before relying on them in production, particularly for correctness and security-sensitive code.

License

Apache 2.0, inherited from the base model. See the Qwen2.5-Coder-14B-Instruct license for details.