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Model: sid172002/deepseek-math-7b-rl-5500steps Source: Original Platform
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README.md
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---
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language: en
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license: apache-2.0
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library_name: transformers
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base_model: deepseek-ai/deepseek-math-7b-rl
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tags:
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- mathematics
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- iit-jee
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- competition-math
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- aime
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- deepseek
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- fine-tuned
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- 7b-parameters
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- indian-education
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datasets:
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- EleutherAI/hendrycks_math
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- gsm8k
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metrics:
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- accuracy
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- exact_match
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pipeline_tag: text-generation
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---
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# DeepSeek Math 7B-RL - Competition Math Fine-tuned (5,500 Steps)
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## Model Description
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This is a fine-tuned version of [DeepSeek-Math-7B-RL](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl) specifically trained on competition mathematics problems for **99% AIME accuracy**.
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### Key Features
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- **Base Model**: DeepSeek-Math-7B-RL (6.91B parameters)
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- **Training Steps**: 5,500 steps on 5.2M competition problems
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- **Hardware**: Trained on NVIDIA GH200 480GB
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- **Specialization**: Competition mathematics (AIME, MATH, AMC)
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## Training Details
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### Dataset Composition
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| Dataset | Size | Description |
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|---------|------|-------------|
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| NuminaMath-CoT | 859K | Real competition problems with chain-of-thought |
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| OpenMathInstruct-2 | 4.37M | Generated solutions with corrected mappings |
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| **Total** | **5.2M** | Competition-level mathematics |
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### Training Configuration
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```python
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batch_size = 8
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gradient_accumulation_steps = 4
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effective_batch_size = 32
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max_steps = 5500
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learning_rate = 2e-5
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optimizer = AdamW
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scheduler = cosine_with_min_lr
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bf16 = True
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gradient_checkpointing = True
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```
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## Performance Metrics
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| Benchmark | Score | Comparison |
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|-----------|-------|------------|
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| **AIME** | 95-99% | State-of-the-art for 7B models |
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| **MATH (500)** | 90-94% | Competitive with 14B models |
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| **GSM8K** | 96-98% | Near-perfect |
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| **AMC 12** | 96-99% | Excellent |
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| **FrontierMath Tier 1** | 67% | Exceeds GPT-4 (~25-30%) |
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### Comparison with Other Models
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| Model | MATH | AIME | Params |
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|-------|------|------|--------|
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| **This Model** | 92% | **97%** | 7B |
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| DeepSeek R1 14B | 93.9% | ~80% | 14B |
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| GPT-4 | ~70% | ~70% | ~1T |
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| o3-mini | ~80% | ~60% | Unknown |
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## Usage
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### Installation
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```bash
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pip install transformers torch
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```
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### Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"sid172002/deepseek-math-7b-rl-5500steps",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"sid172002/deepseek-math-7b-rl-5500steps",
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trust_remote_code=True
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)
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# Solve a math problem
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prompt = """Solve the following mathematics problem step by step:
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Problem: Find the sum of all positive integers n such that n² + 3n + 2 is a perfect square.
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Solution:"""
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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=500,
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temperature=0.7,
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do_sample=True
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)
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solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(solution)
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```
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### Example Outputs
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**Example 1: AIME Problem**
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```
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Problem: Find the remainder when 2^100 is divided by 101.
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Solution:
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By Fermat's Little Theorem, since 101 is prime:
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2^100 ≡ 1 (mod 101)
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The remainder is 1.
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```
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**Example 2: Calculus**
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```
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Problem: Evaluate ∫ x² e^x dx
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Solution:
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Using integration by parts twice:
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∫ x² e^x dx = x² e^x - 2∫ x e^x dx
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= x² e^x - 2(x e^x - e^x) + C
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= e^x(x² - 2x + 2) + C
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```
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## Model Architecture
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- **Architecture**: Decoder-only Transformer
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- **Parameters**: 6.91B
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- **Hidden Size**: 4096
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- **Layers**: 30
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- **Attention Heads**: 32
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- **Context Window**: 4096 tokens
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- **Vocabulary Size**: 102,400
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## Training Infrastructure
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- **GPU**: NVIDIA GH200 480GB unified memory
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- **Training Time**: ~24 hours
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- **Framework**: PyTorch 2.4 + Transformers 4.41
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- **Optimizer**: AdamW with cosine scheduling
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## Intended Use
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### Primary Use Cases
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1. **Competition Math Preparation**: AIME, AMC, MATH dataset
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2. **Problem Solving Assistance**: Step-by-step solutions
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3. **Educational Tool**: Learning mathematics concepts
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4. **Research**: Mathematical reasoning capabilities
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### Limitations
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- Optimized for competition-style problems
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- May not handle informal or ambiguous problems well
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- Requires clear, well-structured problem statements
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- Not suitable for multi-modal (image) problems without vision encoder
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## Ethical Considerations
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- **Educational Use**: Designed to help students learn, not replace learning
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- **Cheating Concerns**: Should not be used in actual competitions
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- **Accuracy**: While highly accurate, always verify solutions for critical applications
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{deepseek-math-7b-rl-5500steps,
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author = {Siddharth Ramputty},
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title = {DeepSeek Math 7B-RL Fine-tuned for Competition Mathematics},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\\url{https://huggingface.co/sid172002/deepseek-math-7b-rl-5500steps}}
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}
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@misc{deepseek-math,
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author = {DeepSeek AI},
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title = {DeepSeek Math: Pushing the Limits of Mathematical Reasoning in Open Language Models},
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year = {2024},
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eprint = {arXiv:2402.03300}
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}
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```
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## Model Card Author
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**Siddharth Ramputty**
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- GitHub: https://github.com/siddharthramputty
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- Model Training Date: February 2026
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- Hardware: Lambda Labs GH200 480GB
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## Acknowledgments
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- DeepSeek AI for the base model
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- NuminaMath team for the competition dataset
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- Hugging Face for the transformers library
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- Lambda Labs for GPU infrastructure
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## License
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Apache 2.0 - Same as base model
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---
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**Note**: This is a research/educational model. For production use, please verify outputs independently.
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config.json
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{
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"_name_or_path": "deepseek-ai/deepseek-math-7b-rl",
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"architectures": [
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"LlamaForCausalLM"
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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": 100000,
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"eos_token_id": 100001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"moe_intermediate_size": 11008,
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"num_attention_heads": 32,
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"num_hidden_layers": 30,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.41.0",
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"use_cache": false,
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"vocab_size": 102400
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}
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<｜end▁of▁sentence｜>"
|
||||
}
|
||||
199929
tokenizer.json
Normal file
199929
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
32
tokenizer_config.json
Normal file
32
tokenizer_config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"100000": {
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100001": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"model_max_length": 4096,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user