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Cherrytest
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README.md
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README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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language:
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- en
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license: mit
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library_name: transformers
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tags:
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- reasoning
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- axolotl
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- r1
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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datasets:
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- ServiceNow-AI/R1-Distill-SFT
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pipeline_tag: text-generation
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model-index:
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- name: DeepSeek-R1-Distill-Llama-3B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 70.93
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 21.45
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 20.92
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 1.45
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 2.91
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 21.98
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/DeepSeek-R1-Distill-Llama-3B
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name: Open LLM Leaderboard
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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# DeepSeek-R1-Distill-Llama-3B
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This model is the distilled version of DeepSeek-R1 on Llama-3.2-3B with R1-Distill-SFT dataset.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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```yaml
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base_model: unsloth/Llama-3.2-3B-Instruct
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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chat_template: llama3
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datasets:
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- path: ./custom_dataset.json
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type: chat_template
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conversation: chatml
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ds_type: json
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add_bos_token: true
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add_eos_token: true
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use_default_system_prompt: false
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special_tokens:
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bos_token: "<|begin_of_text|>"
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eos_token: "<|eot_id|>"
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pad_token: "<|eot_id|>"
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additional_special_tokens:
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- "<|begin_of_text|>"
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- "<|eot_id|>"
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adapter: lora
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lora_model_dir:
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0.1
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lora_target_linear: true
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hub_model_id: suayptalha/DeepSeek-R1-Distill-Llama-3B
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len: true
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micro_batch_size: 2
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gradient_accumulation_steps: 8
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num_epochs: 1
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learning_rate: 2e-5
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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train_on_inputs: false
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group_by_length: false
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bf16: false
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fp16: true
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tf32: false
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gradient_checkpointing: true
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flash_attention: false
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logging_steps: 50
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warmup_steps: 100
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saves_per_epoch: 1
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output_dir: ./finetune-sft-results
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save_safetensors: true
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('suayptalha/DeepSeek-R1-Distill-Llama-3B')
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</details><br>
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# Prompt Template
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You can use Llama3 prompt template while using the model:
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### Llama3
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/suayptalha/DeepSeek-R1-Distill-Llama-3B.git
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<|start_header_id|>system<|end_header_id|>
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{system}<|eot_id|>
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<|start_header_id|>user<|end_header_id|>
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{user}<|eot_id|>
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<|start_header_id|>assistant<|end_header_id|>
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{assistant}<|eot_id|>
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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## Example usage:
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"suayptalha/DeepSeek-R1-Distill-Llama-3B",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("suayptalha/DeepSeek-R1-Distill-Llama-3B")
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SYSTEM_PROMPT = """Respond in the following format:
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<think>
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You should reason between these tags.
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</think>
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Answer goes here...
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Always use <think> </think> tags even if they are not necessary.
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"""
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": "Which one is larger? 9.11 or 9.9?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize = True,
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add_generation_prompt = True,
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return_tensors = "pt",
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).to("cuda")
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output = model.generate(input_ids=inputs, max_new_tokens=256, use_cache=True, temperature=0.7)
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decoded_output = tokenizer.decode(output[0], skip_special_tokens=False)
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print(decoded_output)
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```
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## Output:
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```
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<think>
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First, I need to compare the two numbers 9.11 and 9.9.
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Next, I'll analyze each number. The first digit after the decimal point in 9.11 is 1, and in 9.9, it's 9.
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Since 9 is greater than 1, 9.9 is larger than 9.11.
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</think>
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To determine which number is larger, let's compare the two numbers:
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**9.11** and **9.9**
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1. **Identify the Decimal Places:**
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- Both numbers have two decimal places.
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2. **Compare the Tens Place (Right of the Decimal Point):**
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- **9.11:** The tens place is 1.
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- **9.9:** The tens place is 9.
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3. **Conclusion:**
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- Since 9 is greater than 1, the number with the larger tens place is 9.9.
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**Answer:** **9.9** is larger than **9.11**.
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```
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## Suggested system prompt:
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```
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Respond in the following format:
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<think>
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You should reason between these tags.
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</think>
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Answer goes here...
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Always use <think> </think> tags even if they are not necessary.
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```
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# Parameters
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- lr: 2e-5
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- epochs: 1
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- batch_size: 16
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- optimizer: paged_adamw_8bit
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/suayptalha__DeepSeek-R1-Distill-Llama-3B-details)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |23.27|
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|IFEval (0-Shot) |70.93|
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|BBH (3-Shot) |21.45|
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|MATH Lvl 5 (4-Shot)|20.92|
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|GPQA (0-shot) | 1.45|
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|MuSR (0-shot) | 2.91|
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|MMLU-PRO (5-shot) |21.98|
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# Support
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<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
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