GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
jgalego
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
jgalego
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "jgalego/ada-coder-qwen2.5-7b"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto") messages = [ {"role": "system", "content": "You write Ada 2022 and SPARK code that compiles cleanly with GNAT."}, {"role": "user", "content": "Write a function that returns the greatest common divisor of two Positive numbers."},]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,)out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))It also completes raw Ada source with no chat template, which is how it is scored below.
| Base | Qwen/Qwen2.5-Coder-7B-Instruct |
| Data | r7_dataset.jsonl from the-clanker-lover/steelman-sft-ada, 6,110 instruction/code pairs that compile with GNAT |
| Method | LoRA, r=16, alpha=32, all linear layers, merged |
| Steps | 378 (1.0 epochs), batch size 16, max length 2048 |
| Learning rate | 0.0002, cosine |
| Mean train loss | 0.2128 |
| Hardware | NVIDIA A10G, 69 min |
nuprl/MultiPL-E HumanEval-Ada (humaneval-adb, 157 problems). Greedy decoding from the raw prompt, then built and run with the GNAT flags from MultiPL-E's eval_adb.py. A program passes when every pragma Assert holds.
| Model | n | pass@1 | Compile rate | Runtime errors | Timeouts |
|---|---|---|---|---|---|
| jgalego/ada-coder-qwen2.5-7b | 157 | 0.414 | 0.771 | 56 | 0 |
| jgalego/ada-coder-qwen2.5-7b@grpo-1 | 157 | 0.414 | 0.796 | 60 | 0 |
| jgalego/ada-coder-qwen2.5-7b@sft-2 | 157 | 0.401 | 0.771 | 58 | 0 |
| Qwen/Qwen2.5-Coder-7B-Instruct | 157 | 0.312 | 0.471 | 25 | 0 |
Reference scores reported by the Steelman authors (model card, report) on the same benchmark with their own harness, temperature 0:
| Model | pass@1 | Compile rate |
|---|---|---|
| Steelman 14B v0.3 (Qwen2.5-Coder-14B + R7 data) | 0.459 | 0.854 |
| Steelman 14B v0.2 (Qwen2.5-Coder-14B-Instruct + R6 data) | 0.471 | 0.752 |
| Qwen2.5-Coder-14B, untuned | 0.344 | 0.510 |
-gnata); nothing here runs GNATprove.from transformers import AutoModelForCausalLM, AutoTokenizer repo = "jgalego/ada-coder-qwen2.5-7b"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto") messages = [ {"role": "system", "content": "You write Ada 2022 and SPARK code that compiles cleanly with GNAT."}, {"role": "user", "content": "Write a function that returns the greatest common divisor of two Positive numbers."},]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,)out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))