Instructions to use eivintobias/heartly-qwen-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eivintobias/heartly-qwen-code with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="eivintobias/heartly-qwen-code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eivintobias/heartly-qwen-code")
model = AutoModelForCausalLM.from_pretrained("eivintobias/heartly-qwen-code", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eivintobias/heartly-qwen-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eivintobias/heartly-qwen-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eivintobias/heartly-qwen-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eivintobias/heartly-qwen-code

SGLang

How to use eivintobias/heartly-qwen-code with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "eivintobias/heartly-qwen-code" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eivintobias/heartly-qwen-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "eivintobias/heartly-qwen-code" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eivintobias/heartly-qwen-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eivintobias/heartly-qwen-code with Docker Model Runner:
```
docker model run hf.co/eivintobias/heartly-qwen-code
```

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) and server.py are bundled in this repo (HF clone = flat layout; GitHub = heartly-qwen-code/). Clone it so from reply_formatter import format_reply resolves 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).

Model tree for eivintobias/heartly-qwen-code

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B