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Model Details
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te-sla
Model Tree
unsloth/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
This is the direct-load, merged BF16 form of
te-sla/serbian-emotion-qwen3.5-9b-lora.
It combines the prespecified seed-42 LoRA adapter with
unsloth/Qwen3.5-9B at pinned revision
005429cee5cb648998cf2b70eebdd83175989c9a, so users load one repository rather than loading a base
model and then attaching a PEFT adapter. The repository ID is
te-sla/serbian-emotion-qwen3.5-9b.
This is deployment packaging of the same evaluated model, not another training run, seed, or ranking entry. The LoRA repository remains available as the smaller download; both formats carry the same Round 12 scientific claim. TeslaXLM remains the primary publication encoder.
The model emits one strict JSON array containing zero, one, or two labels in the fixed order
anger, anticipation, disgust, fear, joy, sadness, surprise, trust.
Use this repository when simple one-repository loading is more important than the smaller adapter download. It is intended for research on direct structured Serbian social-media emotion generation. Do not use it for clinical assessment, inference about an author's mental state, employment, law-enforcement, or other high-impact decisions.
merge_and_unload(safe_merge=True) followed by Transformers safe serialization,
preserving BF16 and using shards no larger than 5 GB.The source adapter uses base revision 005429cee5cb648998cf2b70eebdd83175989c9a. Test performance
did not select the uploaded seed or checkpoint.
The merged package inherits the evaluation of the exact source adapter; it is not counted as an additional evaluated model.
| Scope | Rows | Macro F1 | Sample Jaccard | Exact set | Invalid rate |
|---|---|---|---|---|---|
| Test, released seed 42 | 3,000 | 0.526192 | 0.591056 | 0.505333 | 0.000000 |
| Test family mean, 10 seeds | 3,000 per seed | 0.516697 | 0.589383 | 0.507767 | 0.000000 |
Macro-F1 sample SD across the ten seeds was 0.007589; all 30,000 test outputs were valid under the
strict parser. The same test cohort informed earlier rounds, so these are adaptive internal results,
not independent external validation.
This path loads the working merged checkpoint directly—there is no separate base-model or adapter load:
python
import jsonimport torchfrom unsloth import FastModelmodel_id = "te-sla/serbian-emotion-qwen3.5-9b"model, tokenizer = FastModel.from_pretrained(model_name=model_id,max_seq_length=1024,dtype=torch.bfloat16,device_map={"": 0},load_in_4bit=False,load_in_8bit=False,load_in_16bit=True,full_finetuning=False,trust_remote_code=False,)FastModel.for_inference(model)label_order = ["anger", "anticipation", "disgust", "fear","joy", "sadness", "surprise", "trust",]system_prompt = ("Klasifikuj emocije izražene u srpskom tekstu. ""Dozvoljene oznake su: anger, anticipation, disgust, fear, joy, sadness, ""surprise, trust. Za tekst sa emocijom vrati jednu ili dve oznake. Ako ""nijedna dozvoljena emocija nije izražena, vrati prazan niz. Vrati isključivo ""ispravan JSON niz, bez objašnjenja, Markdowna ili oznake neutral. Oznake ""moraju biti u redosledu u kom su navedene.")text = "Danas sam presrećan zbog odličnih vesti."messages = [{"role": "system","content": [{"type": "text", "text": system_prompt}],},{"role": "user","content": [{"type": "text", "text": text}],},]prompt = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True,enable_thinking=False,)inputs = tokenizer(text=[prompt],return_tensors="pt",add_special_tokens=False,).to("cuda")with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):generated = model.generate(**inputs,do_sample=False,num_beams=1,max_new_tokens=32,pad_token_id=tokenizer.pad_token_id,eos_token_id=tokenizer.eos_token_id,)response = tokenizer.batch_decode(generated[:, inputs["input_ids"].shape[1]:],skip_special_tokens=True,)[0].strip()# Strict validation; do not repair malformed output.predicted = json.loads(response)assert isinstance(predicted, list) and len(predicted) <= 2assert len(predicted) == len(set(predicted))assert all(label in label_order for label in predicted)assert predicted == [label for label in label_order if label in predicted]print(predicted)
The example contains the exact evaluated Serbian system prompt, disables thinking, uses greedy decoding with at most 32 new tokens, and rejects malformed, duplicate, unknown, noncanonical, neutral, or more-than-two-label outputs without repair.
artifact_manifest.json: size and SHA-256 for every packaged file except the manifest itself.source_adapter_manifest.json: exact adapter file hashes used for the merge.merge_provenance.json: base revision, adapter source, merge method, versions, and Git commit.merge_smoke_test.json: raw-output hashes, strict-parser status, and pre/post label equivalence on
non-sensitive synthetic prompts.publication_evidence.json: the Round 12 family statistics and released-artifact metrics.The final paper identifier and citation are pending. The verified paper link and shared portfolio citation will be added to both Qwen repositories when available.