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Model: Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model Source: Original Platform
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README.md
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README.md
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---
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base_model: google/gemma-2-2b
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library_name: transformers
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tags:
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- math
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- reasoning
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- sft
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- gemma
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datasets:
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- nohurry/Opus-4.6-Reasoning-3000x-filtered
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language:
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- en
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- th
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license: gemma
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---
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# Gemma-2-2B SFT Reasoning Model
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A supervised fine-tuned version of [`google/gemma-2-2b`](https://huggingface.co/google/gemma-2-2b), trained to produce structured chain-of-thought reasoning on mathematical and logical problems.
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> **Model Lineage:** This SFT model serves as the foundation for downstream training:
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> [**Phonsiri/gemma-2-2b-GRPO-Reasoning-full**](https://huggingface.co/Phonsiri/gemma-2-2b-GRPO-Reasoning-full)
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---
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## Model Highlights
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This model was trained to explicitly separate its reasoning process from its final answer, using a structured output format. It learns the **syntax and structure** of chain-of-thought reasoning before any reinforcement signal is applied.
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**Output Format:**
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| Section | Tag | Description |
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|---|---|---|
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| Chain-of-Thought | `<reasoning> ... </reasoning>` | Step-by-step internal reasoning |
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| Final Answer | `<answer> ... </answer>` | Concise final answer |
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---
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## Training Details
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### Base Model
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Fine-tuned from [`google/gemma-2-2b-it`](https://huggingface.co/google/gemma-2-2b-it) using full parameter fine-tuning (no LoRA/PEFT).
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### Datasets
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Training data was combined from the following sources:
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| Dataset | Type |
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|---|---|
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| [`nohurry/Opus-4.6-Reasoning-3000x-filtered`](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | HuggingFace — Reasoning |
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| `math_combined_2566_2567.json` | Local — Thai math problems |
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| `problems_1_5.json` | Local — Math problems |
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| `problems_6_10.json` | Local — Math problems |
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| `problems_101_125.json` | Local — Math problems |
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| `combined.json` | Local — Combined problems |
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| `all_solutions.json` | Local — Solutions |
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Epochs | 3 |
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| Learning Rate | 2e-5 |
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| LR Scheduler | Cosine |
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| Max Seq Length | 8192 |
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| Batch Size (per device) | 4 |
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| Gradient Accumulation | 4 |
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| Effective Batch Size | 16 |
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| Warmup Ratio | 0.1 |
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| Weight Decay | 0.01 |
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| Precision | bfloat16 |
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| Gradient Checkpointing | Yes |
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| Attention | SDPA |
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| Optimizer | AdamW |
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import torch
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model_id = "Phonsiri/gemma-2-2b-SFT-Reasoning-full-Model"
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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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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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system_prompt = (
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"You are a helpful assistant. Please reason step by step, "
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"and put your thoughts within <reasoning> and </reasoning> tags, "
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"and your final answer within <answer> and </answer> tags."
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)
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prompt = "Solve for x: 3x + 5 = 20"
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messages = [{"role": "user", "content": f"{system_prompt}\n\n{prompt}"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
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with torch.no_grad():
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model.generate(
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**inputs,
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streamer=streamer,
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max_new_tokens=4096,
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temperature=0.6,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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```
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### Example Output
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```
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<reasoning>
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We need to isolate x on one side of the equation.
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Step 1: Subtract 5 from both sides.
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3x + 5 - 5 = 20 - 5
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3x = 15
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Step 2: Divide both sides by 3.
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x = 15 / 3
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x = 5
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</reasoning>
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<answer>
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x = 5
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</answer>
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```
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---
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## Acknowledgements
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**Authors:**
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- **Phonsiri Thabunsri** — [@Phonsiriwillbejommarn](https://github.com/Phonsiriwillbejommarn)
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- **CYP777** — [@CYP777](https://github.com/CYP777)
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**Project Advisor:**
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- **Supaporn Bunrit, Ph.D.** — Suranaree University of Technology
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**Institutions & Credits:**
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- **Suranaree University of Technology (SUT)** — Research support and computing resources
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- **Google DeepMind** — Open-weights Gemma 2 model
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chat_template.jinja
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{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}<start_of_turn>user
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{{ message['content'] }}<end_of_turn>
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{% elif message['role'] == 'assistant' or message['role'] == 'model' %}<start_of_turn>model
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{{ message['content'] }}<end_of_turn>
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{% endif %}{% endfor %}{% if add_generation_prompt %}<start_of_turn>model
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{% endif %}
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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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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2304,
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"initializer_range": 0.02,
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"intermediate_size": 9216,
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"layer_types": [
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 8,
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"num_hidden_layers": 26,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": 4096,
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"tie_word_embeddings": true,
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"transformers_version": "5.2.0",
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"use_bidirectional_attention": null,
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"use_cache": false,
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"vocab_size": 256000
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}
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generation_config.json
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{
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"_from_model_config": true,
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}
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model.safetensors
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tokenizer.json
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tokenizer_config.json
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"backend": "tokenizers",
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"bos_token": "<bos>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<eos>",
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"is_local": false,
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"mask_token": "<mask>",
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"sp_model_kwargs": {},
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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training_args.bin
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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