Heartly Qwen-Code v3
A 1.5B coding LLM with the Heartly hallucination-reduction architecture,
fine-tuned from Qwen2.5-Coder-1.5B with the conversational Stage-5 SFT recipe
(Fix1–4: natural phrasing, single refusal, persona — 5,200 samples in
heartly-qwen-code/sft_dataset_code_v3.jsonl).
v3 builds on the same v1/v2 Stage 1–4 numbers (grammar adoption 100%, boundary-head
AUROC 1.000, critic AUROC 1.000) — same Qwen2.5-Coder-1.5B base, now trained for
multi-turn conversational code chat. See HF_MODEL_CARD.md for
the Stage 1–2 probe/critic results carried over from the identical architecture.
Output grammar
thinking [reasoning] response<decide>speak|stop</decide><verify>known|unknown</verify> [answer] <stop>
Only [answer] should reach the user.
Usage
0. Recommended — chat via the GitHub server (strips the grammar for you)
This model emits the Heartly grammar as ordinary multi-token text (the tags are
not tokenizer special tokens), so some front-ends (e.g. LM Studio) may decode
them mangled. The server.py FastAPI loader on GitHub loads this model and
runs every reply through reply_formatter.py, which canonicalises the tags
and returns only the clean answer.
pip install -r requirements.txt # fastapi + uvicorn + transformers + torch
python server.py --model eivintobias/heartly-qwen-code --port 8000
curl -X POST http://127.0.0.1:8000/chat \
-H "Content-Type: application/json" \
-d '{"prompt":"Write a function that reverses a string"}'
Response: {"model":"eivintobias/heartly-qwen-code","raw":"...<decide>...","reply":"<clean answer>"}.
Quick browser test (no curl): open http://127.0.0.1:8000/ — server.py serves an
HTML chat UI at GET /. The first message lazy-loads the model; code answers render
with real line breaks, and the Heartly grammar is stripped by the reply formatter.
Quick offline test (no server): python chat_smoke.py "Write a function that sorts a list".
📦 Model card source: this file (HF_MODEL_CARD_v3.md). When uploaded to
HuggingFace, copy it to README.md on the hub repo.
1. Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("eivintobias/heartly-qwen-code")
model = AutoModelForCausalLM.from_pretrained(
"eivintobias/heartly-qwen-code", torch_dtype=torch.float32, device_map="cpu"
)
model.eval()
ids = tok.encode("User: Write a function that reverses a string\nAssistant: ", return_tensors="pt")
out = model.generate(**ids, max_new_tokens=256, pad_token_id=tok.eos_token_id, do_sample=False)
raw = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False)
# Strip the grammar -> clean answer:
from reply_formatter import format_reply
print(format_reply(raw))
reply_formatter.py(grammar strip) andserver.pyare bundled in this repo (HF clone = flat layout; GitHub =heartly-qwen-code/). Clone it sofrom reply_formatter import format_replyresolves before the offline example.
Files in this repo
| File | Description |
|---|---|
config.json | Qwen2ForCausalLM (28 layers, d=1536) + heartly_stop_token_id=9495 |
generation_config.json | default generate params |
chat_template.jinja | standard Qwen chat template |
tokenizer.json / tokenizer_config.json | Qwen BPE tokenizer |
model.safetensors | v3 fine-tuned weights (full fine-tune, not a LoRA adapter) |
server.py | FastAPI server: lazy-loads the model; serves /, /health, /chat; strips Heartly grammar via reply_formatter |
reply_formatter.py | strips thinking / <decide> / <verify> / <stop> -> clean answer; unescapes code newlines |
chat_smoke.py | offline load + chat smoke test (no server) |
requirements.txt | torch, transformers, fastapi, uvicorn, sentencepiece, datasets, scikit-learn, numpy, accelerate, huggingface_hub |
Training
- Base: Qwen/Qwen2.5-Coder-1.5B
- Method: full fine-tune (fp16), max-length 512, 2 epochs, freeze bottom 12 layers
- Dataset:
sft_dataset_code_v3.jsonl(5,200 conversational Heartly samples) - GPU: 1× RTX 3090 (24GB)
License
MIT — built on Qwen2.5-Coder (Apache 2.0).