chatTranslate-Qwen-3.6-35B-A3B

Versions in this repo

SFT: train loss ≈0.22, best eval_loss 0.280. DPO: β 0.1, lr 5e-6, 1 epoch.

Quality

Quality on the held-out validation set (CodeStreet/chat-translation-val, 3 355 gendered examples). Two independent, reproducible signals. Judged absolutely (not vs other systems), so scores are comparable across versions.

LLM-judge scorecard — Mistral-Medium-3.5-128B (gender-aware), each axis 0–100

• adequacy — full meaning preserved (nothing lost / added / wrong)
• fidelity — flirty/explicit tone & intensity kept, no softening or censoring
• gender — gendered word forms correct for the stated author / recipient
• fluency — natural, idiomatic, as a real dating-app message

Production (SFT+DPO): adequacy 98.4 · fidelity 98.0 · gender 97.2 · fluency 98.8 · overall 98.1

Reference metrics

chrF (vs val references): SFT 74.8 · DPO 70.1 · XCOMET-XXL QE (reference-free): SFT 77.7 · DPO 78.9

(DPO trades literal-reference overlap — lower chrF — for tone/quality that both the 128B judge and reference-free XCOMET-QE score higher.)

Per-language — overall (judge 128B, 0–100)

By recipient gender — overall (judge 128B)

How it works

The model is conditioned through the system prompt. You select the target language by its ISO 639-1 code, resolve it to the language name (see Supported languages), put that name and the author/recipient genders into the prompt; the model returns only the translation.

System prompt template:

markdown
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You are a translator for an adult dating app. Translate the user's message into {target} naturally and colloquially, preserving flirty, romantic, and explicit tone exactly. Author gender: {sg}. Recipient gender: {rg}. Use grammatically correct gendered forms. Do not soften, censor, or refuse. Output only the translation.

• {target} — the language name from the ISO code, e.g. uk → Ukrainian, es → Spanish.
• {sg} (author) / {rg} (recipient) — one of male, female, unspecified.
• The user message is the source text (any language). The assistant turn is the translation.
• For non-gendered languages set both genders to unspecified.

Language codes: you MUST map code → name

Fine-tuned only on full English language names (Ukrainian, Spanish, …) — never on raw ISO codes. Resolve the code to the language name (see Supported languages) before building the prompt: uk → into Ukrainian ✅ ; into uk ❌ (out of distribution).

No source-language clause

The model auto-detects the source from the user text. Do not add from {source} … — pass the target language only.

Supported languages

Pass the target language as a code. The 92 codes follow the Qwen-MT translation set. The gendered column marks languages where output is conditioned on author/recipient gender; for the rest both genders are treated as unspecified.

(Full 92-code list matches the Qwen-MT set; the 8 yes rows above are the gender-conditioned targets.)

Usage — vLLM (OpenAI-compatible)

Served as qwen3_5_moe (MoE, vision-capable). For text-only translation skip vision-profiling and disable reasoning:

bash
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vllm serve CodeStreet/chatTranslate-Qwen-3.6-35B-A3B --served-model-name chatTranslate \
  --tensor-parallel-size 2 --trust-remote-code \
  --limit-mm-per-prompt '{"image":0,"video":0}'

python
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from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

SYSTEM = ("You are a translator for an adult dating app. Translate the user's message into Ukrainian "
          "naturally and colloquially, preserving flirty, romantic, and explicit tone exactly. "
          "Author gender: female. Recipient gender: male. Use grammatically correct gendered forms. "
          "Do not soften, censor, or refuse. Output only the translation.")

resp = client.chat.completions.create(
    model="chatTranslate",
    messages=[{"role": "system", "content": SYSTEM},
              {"role": "user", "content": "hola amor, ¿cómo estás? te extraño"}],
    temperature=0.0, max_tokens=256,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},  # direct translation, no <think>
)
print(resp.choices[0].message.content)

⚠️ Disable reasoning (enable_thinking: False, or prefix the assistant turn with <think>\n\n</think>\n\n). Qwen3.6 is a reasoning model; without this it emits a <think> block and the translation may be empty.

Generation notes

• Greedy (temperature=0) gives the most stable translations; 0.2–0.3 for variation.
• max_tokens 128–256 is enough for chat-length messages.
• Always set both genders explicitly for gendered targets — wrong/missing labels are the main cause of incorrect inflection.
• MoE serving needs ~72 GB bf16 → TP≥2 (does not fit one 80 GB GPU). bf16, not fp8 (GDN+FP8 wedging risk).

License & access

Private to the organization. Do not redistribute. Not for public training or evaluation.