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Model: jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 Source: Original Platform
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README.md
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---
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language:
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- fr
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- en
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library_name: transformers
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tags:
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- dpo
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- post-training
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- french
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- alignment
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- model-merging
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- qwen3
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- chocolatine
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- comparia
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- jpacifico/comparia-dpo-pairs-bt-6k
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- jpacifico/french-orca-dpo-pairs-revised
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---
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# Chocolatine-2-4B-Instruct-DPO-v2.1
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**Chocolatine-2-4B-Instruct-DPO-v2.1** is a post-trained version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), designed to improve instruction-following, reasoning, and overall performance in French, while preserving strong multilingual capabilities.
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In my evaluation setup, it delivers consistent gains across the tested French benchmarks, pointing to a broad improvement in French capabilities.
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Although the post-training pipeline focuses on French preference data, no degradation is observed on English tasks, and slight improvements are sometimes seen, suggesting positive cross-lingual transfer.
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Optimized variants (MLX, GGUF) are also available, making the model particularly suitable for local inference.
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## Model Overview
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- **Base model:** Qwen/Qwen3-4B-Instruct-2507
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- **Parameters:** 4.0B
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- **Context Length:** 262,144 natively
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- **Post training methods:** DPO + Model Merging
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Note: This model supports only non-thinking mode and does not generate `<think></think>` blocks in its outputs.
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This design is consistent with the goals of the post-training setup, which favors a compact dense instruct model focused on direct generation efficiency and practical downstream use.
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For use cases requiring explicit reasoning traces or structured thinking outputs, Qwen/Qwen3.5-4B (thinking mode) is recommended.
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**Model Variants**
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- Chocolatine-2-4B-Instruct-DPO-v2.1 (this repo): Contains the retrainable weights in BF16 format
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- Quantized GGUF versions : [Q4_K_M](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-Q4_K_M-GGUF) / [Q8_0](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-Q8_0-GGUF) and more from mradermacher [here](https://huggingface.co/mradermacher/Chocolatine-2-4B-Instruct-DPO-v2.1-GGUF)
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- MLX (optimized for Apple silicon): [4Bit](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-mlx-4Bit) / [6Bit](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-mlx-8Bit)
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**Ollama** : In addition to the Hugging Face release, quantized 4-bit and 8-bit variants are also available [here](https://ollama.com/jpacifico/chocolatine-2.1) on Ollama for convenient local inference.
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## Benchmarks
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The results indicate a consistent improvement across the tested French benchmarks, covering several capability types. This suggests a broad gain in French performance, while English results remain overall stable.
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| Benchmark fr | Qwen3-4B-Instruct-2507 (base) | Chocolatine-2-4B-Instruct-DPO-v2.1 |
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|---|---:|---:|
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| gpqa-fr:diamond | 28.93 | **32.49** |
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| french_bench_arc_challenge | 47.13 | **49.79** |
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| french_bench_grammar | 70.59 | **72.27** |
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| french_bench_boolqa | 88.76 | **89.89** |
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| french_bench_hellaswag | 56.99 | **58.03** |
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| global_mmlu_fr | 63.75 | **64.75** |
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| xwinograd_fr | 66.27 | **67.47** |
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| fr_mt_bench | 6.22 | **6.44** |
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*FR-MT-Bench* evaluation is performed on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french), using [multilingual-mt-bench](https://github.com/jpacifico/multilingual_mt_bench) with OpenAI/GPT-5 as the LLM judge.
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*global_mmlu_fr*, *xwinograd_fr* and *french_bench* results were obtained using [EleutherAI LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) in a **0-shot** evaluation setting.
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*gpqa-fr:diamond* using LightEval/vLLM via [kurakurai/Luth](https://github.com/kurakurai/Luth.git) process eval.
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| Benchmark eng | Qwen3-4B-Instruct-2507 (base) | Chocolatine-2-4B-Instruct-DPO-v2.1 |
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|---|---:|---:|
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| arc_challenge | **58.79** | 58.45 |
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| hellaswag | 69.08 | **70.16** |
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| boolq | 84.80 | **85.32** |
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| gpqa_diamond_zeroshot | **38.89** | 38.38 |
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English benchmark results were obtained using [EleutherAI LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) in a **0-shot** evaluation setting.
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## Training & Alignment Pipeline
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Chocolatine-2-4B-Instruct-DPO-v2.1 is derived from [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) using a multi-step post-training pipeline:
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**Stage 1 – DPO (Compar:IA adaptation)**
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Direct Preference Optimization (DPO) on a DPO-adapted version of **[Compar:IA](https://comparia.beta.gouv.fr/datasets)** data, derived from the preference dataset [comparia-votes](https://huggingface.co/datasets/ministere-culture/comparia-votes), part of a public initiative led by the Ministry of Culture (French gov). Previous iterations of the Chocolatine model series also were selected as part of this initiative.
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I constructed an original DPO dataset from these votes by transforming them into preference pairs (chosen / rejected), with additional filtering and formatting steps to make them suitable for DPO fine-tuning.
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Two dataset variants were created ([6k](https://huggingface.co/datasets/jpacifico/comparia-dpo-pairs-bt-6k) and [13k](https://huggingface.co/datasets/jpacifico/comparia-dpo-pairs-bt-13k) preference pairs).
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The **6k variant** was used for the DPO training reported in this release.
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**Stage 2 – DPO (French-ORCA pairs)**
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A second DPO stage using a french-version of ORCA preference pairs, based on the dataset **[jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised)**, commonly used in the Chocolatine training pipeline.
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This stage further improves : general instruction alignment, robustness across tasks, cross-lingual capabilities.
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**Stage 3 – Model Merging (MergeKit + TIES)**
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The resulting checkpoints were merged using **MergeKit** with the TIES method.
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TIES merging: selects task-relevant parameter updates, reduces destructive interference between models and preserves base model stability.
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MergeKit configuration:
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```yaml
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# ties2 recipe
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models:
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- model: jpacifico/Qwen3-4B-Instruct-DPO-test2
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parameters:
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density: 0.5
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weight: 0.5
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- model: jpacifico/Qwen3-4B-Instruct-DPO-test-b3
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parameters:
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density: 0.5
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weight: 0.5
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merge_method: ties
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base_model: Qwen/Qwen3-4B-Instruct-2507
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parameters:
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normalize: false
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int8_mask: true
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dtype: bfloat16
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```
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## Usage
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The following contains a code snippet illustrating how to use the model generate content based on given inputs.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1"
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# load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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# prepare the model input
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# conduct text completion
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=16384
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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content = tokenizer.decode(output_ids, skip_special_tokens=True)
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print("content:", content)
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```
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## Limitations
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The Chocolatine-2 model series is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.
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It does not have any moderation mechanism.
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Developed by: Jonathan Pacifico, 2026
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Model type: LLM
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Language(s) (NLP): French, English
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License: Apache-2.0
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Made with ❤️ in France
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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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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"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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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.53.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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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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model-00001-of-00009.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2e8b8e58356bd3ee8532f858bfb6566aa8413fe9aa614ca2a0c772099529d4ad
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size 979780560
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version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:eefaa2114fee22fdba995e39a89e9efaa52275e50a3b4f57acb4f100117e2814
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||||||
|
size 983094568
|
||||||
3
model-00003-of-00009.safetensors
Normal file
3
model-00003-of-00009.safetensors
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|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:acf6eb079769dd435f6b738746f179bd6635733cb20ed1d06af10443e170c4e1
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||||||
|
size 988342432
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||||||
3
model-00004-of-00009.safetensors
Normal file
3
model-00004-of-00009.safetensors
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|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:d26b88421cb79f4a3ddc04a2fd12c214184edd138fef9efe2424cfb43c11d87b
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||||||
|
size 954258352
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||||||
3
model-00005-of-00009.safetensors
Normal file
3
model-00005-of-00009.safetensors
Normal file
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|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
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oid sha256:0b85a997b10a2fb9912c9b95c498a6e019ddb38ea8ba147f52d8813ee81361c6
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||||||
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size 959506960
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||||||
3
model-00006-of-00009.safetensors
Normal file
3
model-00006-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
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||||||
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size 959506952
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3
model-00007-of-00009.safetensors
Normal file
3
model-00007-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
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size 983094576
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3
model-00008-of-00009.safetensors
Normal file
3
model-00008-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
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||||||
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size 988342384
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||||||
3
model-00009-of-00009.safetensors
Normal file
3
model-00009-of-00009.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:9997a31b79e21ae6ada6844d8daffcaad1e5aa1298c9926791ba7f504d7c26ff
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||||||
|
size 249054736
|
||||||
1
model.safetensors.index.json
Normal file
1
model.safetensors.index.json
Normal file
File diff suppressed because one or more lines are too long
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
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239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
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|
||||||
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151648": {
|
||||||
|
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|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
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|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
"special": true
|
||||||
|
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|
||||||
|
"151650": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151653": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151665": {
|
||||||
|
"content": "<tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
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|
||||||
|
"rstrip": false,
|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"151666": {
|
||||||
|
"content": "</tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151667": {
|
||||||
|
"content": "<think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151668": {
|
||||||
|
"content": "</think>",
|
||||||
|
"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,
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"model_max_length": 1010000,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null,
|
||||||
|
"add_bos_token": false
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user