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Model: toroe/SmolLM-3B-Science-EN Source: Original Platform
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265
README.md
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README.md
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---
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language:
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- en
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license: other
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base_model: HuggingFaceTB/SmolLM3-3B
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tags:
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- sft
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- instruction-tuning
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- reasoning
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- long-context
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- fsdp
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- transformers
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- liger-kernel
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- english
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datasets:
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- DGurgurov/Nemotron-Multilingual-Reasoning
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metrics:
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pipeline_tag: text-generation
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---
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# SmolLM3-3B — English Reasoning Instruction Fine-Tune (Nemotron Multilingual Reasoning)
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## Model Description
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This model is a **Supervised Fine-Tuned (SFT)** version of:
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`HuggingFaceTB/SmolLM3-3B`
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It was trained on the **English (`en`) split** of:
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`DGurgurov/Nemotron-Multilingual-Reasoning`
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The purpose of this fine-tune is to improve:
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- English instruction following
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- multi-step reasoning
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- long-context chat behavior
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The dataset was converted into structured chat conversations and optimized using **completion-only loss**, meaning only the assistant’s responses contributed to the training objective.
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### Key Characteristics
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- Base model: SmolLM3-3B
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- Language: English specialization
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- Context length during training: **16,384 tokens**
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- Chat formatted conversations
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- Packed sequences
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- Long-context reasoning tuning
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---
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## Intended Uses
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### Suitable
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- Conversational assistants
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- Instruction-following agents
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- Reasoning tasks
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- Educational tutoring
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- Long-document Q&A
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- Research on small long-context LLMs
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### Not Suitable
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- Medical or legal advice
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- Autonomous decision making
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- Safety-critical systems
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- Financial decision automation
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---
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## Training Data
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Dataset:
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`DGurgurov/Nemotron-Multilingual-Reasoning`
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Processing configuration:
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- Language filter: **English only**
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- Converted to chat messages (`prepare_messages=True`)
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- Assistant-only loss masking (`completion_only_loss=True`)
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User and system prompts were masked during training; only assistant tokens produced gradients.
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Please consult the dataset card for data provenance and limitations.
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---
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## Training Procedure
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Training used **HuggingFace Accelerate with Fully Sharded Data Parallel (FSDP)** across 8 processes.
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### Core Setup
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- Method: Supervised fine-tuning (SFT)
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- Epochs: **3**
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- Max sequence length: **16,384**
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- Packing: enabled
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- Precision: **bfloat16**
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- Gradient checkpointing: enabled
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- Liger kernel: enabled
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- Distributed training: FSDP
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---
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### Optimization
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- Optimizer: `adamw_torch_fused`
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- Batch size per device: 4
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- Gradient accumulation: 4
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- Effective batch size per GPU: 16 sequences / step
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- Weight decay: 0.05
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Learning rate schedule:
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- Scheduler: `cosine_with_min_lr`
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- Warmup ratio: 0.05
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- Minimum learning rate: 5e-6
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---
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### Logging & Checkpoints
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- Logging: every 5 steps
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- Checkpoint: every 450 steps
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- Tracking: Weights & Biases
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- Token accuracy logged during training
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---
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### Data Processing
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- Dataset preprocessing workers: 16
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- Chat formatting: enabled
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- Dataset preparation: enabled
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- Language split: `en`
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---
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## Usage
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### Transformers Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "YOUR_USERNAME/YOUR_MODEL_REPO"
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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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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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Explain why the sky is blue."}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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**Important:**
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Use `apply_chat_template()` when prompting. The model was trained on chat-formatted conversations and performance will degrade without it.
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---
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## Evaluation
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During training, **token accuracy** was logged as a diagnostic metric.
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Token accuracy:
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- helps monitor training stability
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- is **not** a benchmark score
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- does not measure reasoning quality
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For meaningful evaluation, use:
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- instruction-following benchmarks
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- reasoning datasets
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- long-context tasks
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|
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---
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## Limitations
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- May hallucinate incorrect information
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- Reasoning chains may contain logical mistakes
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- Performance near 16k tokens depends heavily on prompt structure
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- Smaller model → less world knowledge than large LLMs
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- Not suitable for safety-critical deployment
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---
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## Bias & Safety
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The model inherits biases from:
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- the base model
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- the training dataset
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Recommended mitigations:
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- moderation filtering
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- safety-oriented system prompts
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- human oversight in sensitive use cases
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---
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## License
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||||
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This is a derivative model of:
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`HuggingFaceTB/SmolLM3-3B`
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The original base model license and restrictions apply, along with dataset terms.
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Verify compatibility before commercial usage.
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---
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## Reproducibility (Training Arguments)
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```text
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accelerate launch --use_fsdp --num_processes 8 --config_file sft/my_config.yaml sft/sft_trainer.py
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--model_name HuggingFaceTB/SmolLM3-3B
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--tokenizer_name HuggingFaceTB/SmolLM3-3B
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--dataset_path DGurgurov/Nemotron-Multilingual-Reasoning
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--skip_prepare_dataset False
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--lang_split en
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--prepare_messages True
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--completion_only_loss True
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--max_length 16384
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```
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---
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## Citation
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||||
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If you use this model, please cite:
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||||
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- `HuggingFaceTB/SmolLM3-3B`
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- `DGurgurov/Nemotron-Multilingual-Reasoning`
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---
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## Acknowledgements
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||||
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- HuggingFaceTB — SmolLM3 base model
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- Nemotron Multilingual Reasoning dataset authors
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- HuggingFace Accelerate and Transformers libraries
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94
chat_template.jinja
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chat_template.jinja
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{# ───── defaults ───── #}
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{%- if enable_thinking is not defined -%}
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{%- set enable_thinking = true -%}
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{%- endif -%}
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{# ───── reasoning mode ───── #}
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{%- if enable_thinking -%}
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{%- set reasoning_mode = "/think" -%}
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{%- else -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- endif -%}
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{# ───── header (system message) ───── #}
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{{- "<|im_start|>system\n" -}}
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{%- if messages[0].role == "system" -%}
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{%- set system_message = messages[0].content -%}
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{%- if "/no_think" in system_message -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- elif "/think" in system_message -%}
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{%- set reasoning_mode = "/think" -%}
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{%- endif -%}
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{%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
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{%- endif -%}
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{%- if "/system_override" in system_message -%}
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{{- custom_instructions.replace("/system_override", "").rstrip() -}}
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{{- "<|im_end|>\n" -}}
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{%- else -%}
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{{- "## Metadata\n\n" -}}
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{{- "Knowledge Cutoff Date: June 2025\n" -}}
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{%- set today = strftime_now("%d %B %Y") -%}
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{{- "Today Date: " ~ today ~ "\n" -}}
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{{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
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{{- "## Custom Instructions\n\n" -}}
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{%- if custom_instructions -%}
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{{- custom_instructions + "\n\n" -}}
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{%- elif reasoning_mode == "/think" -%}
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{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
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{%- else -%}
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{{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
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{%- endif -%}
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{%- if xml_tools or python_tools or tools -%}
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{{- "### Tools\n\n" -}}
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{%- if xml_tools or tools -%}
|
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{%- if tools -%}
|
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{%- set xml_tools = tools -%}
|
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{%- endif -%}
|
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{%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
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{%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
|
||||
{%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
|
||||
{%- endfor -%}
|
||||
{%- set xml_tool_string = ns.xml_tool_string + "</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>" -%}
|
||||
{{- xml_tool_string -}}
|
||||
{%- endif -%}
|
||||
{%- if python_tools -%}
|
||||
{%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
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||||
{%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
|
||||
{%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
|
||||
{%- endfor -%}
|
||||
{%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
|
||||
{{- python_tool_string -}}
|
||||
{%- endif -%}
|
||||
{{- "\n\n" -}}
|
||||
{{- "<|im_end|>\n" -}}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{# ───── main loop ───── #}
|
||||
{%- for message in messages -%}
|
||||
{%- set content = message.content if message.content is string else "" -%}
|
||||
{%- if message.role == "user" -%}
|
||||
{{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }}
|
||||
{%- elif message.role == "assistant" -%}
|
||||
{% generation %}
|
||||
{%- if reasoning_mode == "/think" -%}
|
||||
{{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
|
||||
{%- else -%}
|
||||
{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
|
||||
{%- endif -%}
|
||||
{% endgeneration %}
|
||||
{%- elif message.role == "tool" -%}
|
||||
{{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{# ───── generation prompt ───── #}
|
||||
{%- if add_generation_prompt -%}
|
||||
{%- if reasoning_mode == "/think" -%}
|
||||
{{ "<|im_start|>assistant\n" }}
|
||||
{%- else -%}
|
||||
{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
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108
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"SmolLM3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"dtype": "float32",
|
||||
"eos_token_id": 128012,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 65536,
|
||||
"max_window_layers": 28,
|
||||
"mlp_bias": false,
|
||||
"model_type": "smollm3",
|
||||
"no_rope_layer_interval": 4,
|
||||
"no_rope_layers": [
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
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|
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scheduler.pt
Normal file
3
scheduler.pt
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:659b0e002e8b6eac344ffebc6865379e0d42cc97e40742c84909c0ca51df36c9
|
||||
size 1465
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7b6a500b662a34eb3f0374db856ba4ad7de4c81040571d78dc0d357238930005
|
||||
size 17208819
|
||||
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3895
trainer_state.json
Normal file
3895
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:237a182bd960fe23a62d6d6e653e1798d172bb2d1db303093d61702f32fbfa31
|
||||
size 6417
|
||||
Reference in New Issue
Block a user