README.md

---
language:
  - tr
  - en
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3.5-9B
tags:
  - turkish
  - instruct
  - fine-tuned
  - lora
  - gguf
  - llama-cpp
  - text-generation
  - conversational
  - qwen3.5
pipeline_tag: text-generation
model-index:
  - name: lale-9b-2603
    results:
      - task:
          type: text-generation
          name: Turkish Language Understanding
        dataset:
          name: terazi
          type: custom
        metrics:
          - name: core
            type: accuracy
            value: 0.516
          - name: tool
            type: accuracy
            value: 0.444
          - name: fin
            type: accuracy
            value: 0.454
          - name: legal
            type: accuracy
            value: 0.376
---

# lale-9b-2603

**lale** (Turkish for "tulip") is a Turkish instruction-following language model fine-tuned from [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B). It is designed to be the best Turkish language model at its size class, with strong performance in general knowledge, reasoning, tool use, grammar, finance, and legal domains.

## Model Details

| Property | Value |
|---|---|
| Base model | Qwen/Qwen3.5-9B |
| Method | LoRA SFT (r=32, alpha=32, bf16) |
| Training data | 118,355 Turkish instruction examples (~113M tokens) |
| Epochs | 3 |
| Final loss | 0.282 |
| Training time | ~120 hours on 1x RTX 4090 |
| Parameters | 9.5B total, 58M trainable (0.61%) |

## Available Formats

| Format | Size | Use case |
|---|---|---|
| `merged/` | 18 GB | Full bf16 for further fine-tuning or vLLM serving |
| `gguf/lale-9b-q8_0.gguf` | 8.9 GB | High quality inference with llama.cpp / Ollama |
| `gguf/lale-9b-q4_k_m.gguf` | 5.3 GB | Fast inference on consumer hardware |
| `adapter/` | 242 MB | LoRA adapter to apply on base Qwen3.5-9B |

## Training Data

The training data consists of 118,355 synthetic Turkish instruction-response pairs generated using Claude Opus 4.6 and Claude Sonnet 4.6 via AWS Bedrock, across 21 categories in 3 rounds:

**Round 1 (Sonnet, 61.6K examples):** general, reasoning, tool_use, tool_use_advanced, finance, legal, code, translation

**Round 2 (Opus, 37.1K examples):** math, math_cot, multi_turn, tool_use_mcp, distill_reasoning, conversation_persona, reasoning_v2, code_v2

**Round 3 (Opus+Sonnet, 19.7K examples):** multi_step_tool, grammar_drill, error_recovery, legal_terms, translation_pro

All data was filtered for format validity, length bounds, exact deduplication, and tool-use message normalization.

## Benchmark Results (terazi)

Evaluated using the [terazi](https://github.com/selimozten/terazi) Turkish language model benchmark suite.

### lale-9b-2602 vs lale-9b-2603

| Category | 2602 (98K data) | 2603 (118K data) | Change |
|---|---|---|---|
| **core** | 0.511 | **0.516** | +1.0% |
| common_sense | 0.970 | **0.980** | +1.0% |
| reading_comp | 0.535 | 0.512 | -4.3% |
| grammar | 0.288 | **0.337** | **+17.0%** |
| translation | 0.342 | 0.333 | -2.6% |
| summarization | 0.421 | 0.417 | -1.0% |
| **tool** | 0.411 | **0.444** | **+8.0%** |
| api_call | 0.557 | **0.586** | +5.2% |
| multi_step | 0.075 | **0.168** | **+124%** |
| param_extraction | 0.506 | 0.482 | -4.7% |
| error_recovery | 0.229 | 0.215 | -6.1% |
| **fin** | 0.492 | 0.454 | -7.7% |
| sentiment | 0.744 | 0.592 | -20.4% |
| numerical_reasoning | 0.524 | **0.557** | +6.3% |
| term_understanding | 0.226 | **0.252** | +11.5% |
| **legal** | n/a | **0.376** | new |

### Key Improvements
- **multi_step tool use: +124%** -- from targeted R3 multi_step_tool training data
- **grammar: +17%** -- from R3 grammar_drill exercises (vowel harmony, suffix ordering, conjugation)
- **tool use overall: +8%** -- from additional tool_use_mcp and multi_step_tool categories
- **numerical_reasoning: +6.3%** -- from math and math_cot data
- **term_understanding: +11.5%** -- from legal_terms and fin_analysis data

## Usage

### With llama.cpp

```bash
llama-server -m lale-9b-q8_0.gguf -ngl 99 --reasoning-budget 0 -c 4096
```

Note: `--reasoning-budget 0` disables Qwen3.5's thinking mode, which puts output in `reasoning_content` instead of `content`.

### With Ollama

Create a Modelfile:
```
FROM ./lale-9b-q8_0.gguf
PARAMETER num_ctx 4096
```

```bash
ollama create lale -f Modelfile
ollama run lale
```

### With transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "comarproject/lale-9b-2603",
    subfolder="merged",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "comarproject/lale-9b-2603",
    subfolder="merged",
)

messages = [{"role": "user", "content": "Turkiye'nin baskenti neresidir?"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Technical Notes

- Qwen3.5-9B is a unified VLM (vision-language model) with Mamba/hybrid layers. We train only the language components.
- Training data includes normalized tool-use formats: `tool_call`/`tool_result` roles are remapped to standard `assistant`/`tool`, and `content: null` is allowed for OpenAI-style function calling messages.
- LoRA targets: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Optimizer: AdamW 8-bit, cosine LR schedule, warmup 10%
- Sample packing enabled (required patching Unsloth's VLM detection for Qwen3.5)

## Limitations

- Trained primarily on synthetic data from Claude models; may reflect Claude's style and biases
- Context window limited to 2048 tokens during training (base model supports 128K)
- Sentiment analysis regressed from 2602 (-20%) -- may need targeted data for this subcategory
- Some long legal/financial prompts may exceed the trained context length

## License

Apache 2.0

## Citation

```bibtex
@misc{lale-9b-2603,
  title={lale-9b-2603: Turkish Instruction Model Distilled from Frontier Models},
  author={Selim Ozten},
  year={2026},
  url={https://huggingface.co/comarproject/lale-9b-2603}
}
```