Instructions to use N11100/deepseek-r1-python-code-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use N11100/deepseek-r1-python-code-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B") model = PeftModel.from_pretrained(base_model, "N11100/deepseek-r1-python-code-ft") - Transformers
How to use N11100/deepseek-r1-python-code-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="N11100/deepseek-r1-python-code-ft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("N11100/deepseek-r1-python-code-ft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use N11100/deepseek-r1-python-code-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "N11100/deepseek-r1-python-code-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N11100/deepseek-r1-python-code-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/N11100/deepseek-r1-python-code-ft
- SGLang
How to use N11100/deepseek-r1-python-code-ft with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "N11100/deepseek-r1-python-code-ft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N11100/deepseek-r1-python-code-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "N11100/deepseek-r1-python-code-ft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N11100/deepseek-r1-python-code-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use N11100/deepseek-r1-python-code-ft with Docker Model Runner:
docker model run hf.co/N11100/deepseek-r1-python-code-ft
Entrenamiento completado
Browse files- .gitattributes +1 -0
- README.md +60 -0
- chat_template.jinja +1 -0
- tokenizer.json +3 -0
- tokenizer_config.json +15 -0
.gitattributes
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README.md
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---
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library_name: peft
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license: mit
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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tags:
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- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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- lora
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- transformers
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pipeline_tag: text-generation
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model-index:
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- name: deepseek-r1-python-code-ft
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# deepseek-r1-python-code-ft
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This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- PEFT 0.19.1
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- Transformers 5.9.0
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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chat_template.jinja
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<|User|>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<|Assistant|><|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<|tool▁call▁end|>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<|tool▁call▁end|>'}}{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>' + message['content'] + '<|end▁of▁sentence|>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<|Assistant|>' + content + '<|end▁of▁sentence|>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<|tool▁outputs▁begin|><|tool▁output▁begin|>' + message['content'] + '<|tool▁output▁end|>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<|tool▁output▁begin|>' + message['content'] + '<|tool▁output▁end|>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<|Assistant|><think>\n'}}{% endif %}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:f624f8136bc57382156e7595e8ea404a66fbce12e3181b933ec2459bda3a71be
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size 11421907
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tokenizer_config.json
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{
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"add_prefix_space": null,
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"backend": "tokenizers",
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"bos_token": "<|begin▁of▁sentence|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|end▁of▁sentence|>",
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"is_local": false,
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"legacy": true,
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"local_files_only": false,
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"model_max_length": 16384,
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"pad_token": "<|end▁of▁sentence|>",
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"sp_model_kwargs": {},
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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