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Model: GemMaroc/Qwen2.5-7B-Instruct-darija Source: Original Platform
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---
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library_name: transformers
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tags:
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- MoroccanArabic
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- Darija
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- GemMaroc
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- conversational
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- qwen
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pipeline_tag: text-generation
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datasets:
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- GemMaroc/TULU-3-50k-darija-english
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language:
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- ar
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- ary
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- en
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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---
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# Model Card for Qwen2.5-7B-Instruct-darija
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# Qwen2.5-7B-Instruct-darija
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Unlocking **Moroccan Darija** proficiency in a compact and efficient large language model, trained with a _minimal-data, green-AI_ recipe that preserves Qwen2.5-7B-Instruct's strong reasoning abilities while adding fluent Darija generation.
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---
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## Model at a glance
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| **Parameter** | **Value** |
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| ------------------- | ----------------------------------------------------------------------------------------------------- |
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| **Model ID** | `GemMaroc/Qwen2.5-7B-Instruct-darija` |
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| **Base model** | [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| **Architecture** | Decoder-only Transformer (Qwen2.5) |
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| **Parameters** | 7 billion |
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| **Context length** | 32,768 tokens |
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| **Training regime** | Supervised fine-tuning (LoRA → merged) on 50K high-quality Darija/English instructions TULU-50K slice |
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| **License** | Apache 2.0 |
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---
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## Why another Darija model?
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- **Inclusive AI** > 36 million speakers of Moroccan Arabic remain underserved by open LLMs.
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- **Quality-over-quantity** A carefully curated 50 K instruction set surfaces Darija competence without sacrificing cross-lingual reasoning.
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- **Green AI** Qwen2.5-7B-Instruct-darija achieves competitive Darija scores using minimal energy.
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- **Efficiency** 7B parameters provide excellent performance-to-size ratio for resource-constrained environments.
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---
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## Benchmark summary
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### Darija Benchmarks
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| Model | Darija MMLU | Darija HellaSwag | Sentiment Analysis | GSM8K Darija | Summarization (chrF) | ROUGE-1 | ROUGE-L | BERTScore |
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| ------------------------------ | ----------- | ---------------- | ------------------ | ------------ | -------------------- | ------- | ------- | --------- |
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| Qwen2.5-7B-Instruct | 44.9 % | 38.5 % | 63.6 % | 43.9 % | 26.5 | 9.4 | 9.1 | 36.7 |
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| **Qwen2.5-7B-Instruct-darija** | **52.7 %** | **45.5 %** | 60.4 % | **69.8 %** | **27.4** | 8.2 | 8.0 | **39.0** |
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### English Benchmarks
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| Model | MMLU | TruthfulQA | HellaSwag | GSM8K @5 | GSM8K Gen |
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| ------------------------------ | ---------- | ---------- | ---------- | -------- | --------- |
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| Qwen2.5-7B-Instruct | 68.7 % | 63.1 % | 65.4 % | 75.8 % | 90.1 % |
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| **Qwen2.5-7B-Instruct-darija** | **70.0 %** | 53.6 % | **73.9 %** | 74.6 % | 87.2 % |
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<sub>Zero-shot accuracy; full table in the paper.</sub>
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---
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model_id = "GemMaroc/Qwen2.5-7B-Instruct-darija"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto"
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)
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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device_map="auto",
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max_new_tokens=1024,
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temperature=0.7,
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repetition_penalty=1.2,
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no_repeat_ngram_size=3,
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)
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messages = [
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{"role": "user", "content": "شنو هي نظرية 'butterfly effect'؟ فسّرها بدارجة ونقّط مثال بسيط."}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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print(pipe(prompt)[0]["generated_text"][len(prompt):])
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```
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### Chat template (Qwen2.5 format)
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The tokenizer provides a baked-in Jinja template that starts with a **begin-of-sequence** token (`<|im_start|>`), then alternates user/model turns, each wrapped by `<|im_start|>` … `<|im_end|>` markers. When you set `add_generation_prompt=True` it ends after the opening model tag so the model can continue:
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```
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<|im_start|>user
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{user message}<|im_end|>
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<|im_start|>assistant
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```
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The assistant will keep generating tokens until it decides to emit `<|im_end|>`.
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```python
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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```
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No manual token juggling required—the call above handles BOS, turn delimiters, and newline placement automatically.
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---
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Pre-quantised checkpoints will be published under the same repo tags (`qwen2.5-7b-darija-awq-int4`, `qwen2.5-7b-darija-gguf-q4_k_m`).
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---
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## Training recipe (one-paragraph recap)
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1. **Data** Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross-lingual robustness.
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2. **LoRA SFT** Rank 16, α = 32, 3 epochs, bf16, context 32,768.
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3. **Merge & push** Merge LoRA into base weights (`peft.merge_and_unload`), convert to safetensors, upload.
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---
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## Limitations & ethical considerations
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- Sentiment and abstractive summarisation still trail state-of-the-art.
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- Tokeniser is unchanged; rare Darija spellings may fragment.
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- Model may inherit societal biases present in pre-training data.
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- No RLHF / RLAIF safety alignment yet – apply a moderation layer in production.
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---
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## Citation
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If you use Qwen2.5-7B-Instruct-darija in your work, please cite:
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```bibtex
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@misc{skiredj2025gemmarocunlockingdarijaproficiency,
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title={GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data},
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author={Abderrahman Skiredj and Ferdaous Azhari and Houdaifa Atou and Nouamane Tazi and Ismail Berrada},
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year={2025},
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eprint={2505.17082},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.17082},
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}
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```
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added_tokens.json
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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||||
{{- '<|im_end|>\n' }}
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||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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||||
{{- '<|im_start|>user' }}
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||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
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||||
{{- message.content }}
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||||
{{- '\n</tool_response>' }}
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||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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||||
{{- '<|im_end|>\n' }}
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||||
{%- endif %}
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||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
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||||
{{- '<|im_start|>assistant\n' }}
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||||
{%- endif %}
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||||
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 3584,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 18944,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
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"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
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"rope_theta": 1000000.0,
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
||||
"transformers_version": "4.56.1",
|
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"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
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}
|
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
|
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"top_p": 0.8,
|
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"transformers_version": "4.56.1"
|
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}
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user