Instructions to use deepseek-ai/DeepSeek-Math-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-Math-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-Math-V2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Math-V2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use deepseek-ai/DeepSeek-Math-V2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/DeepSeek-Math-V2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/DeepSeek-Math-V2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-Math-V2
- SGLang
How to use deepseek-ai/DeepSeek-Math-V2 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 "deepseek-ai/DeepSeek-Math-V2" \ --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": "deepseek-ai/DeepSeek-Math-V2", "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 "deepseek-ai/DeepSeek-Math-V2" \ --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": "deepseek-ai/DeepSeek-Math-V2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-Math-V2 with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-Math-V2
Fix chat_template crash when assistant message omits the `content` key
Browse files## Fix `chat_template` to handle assistant messages without a `content` key
**⚠️ This template will start crashing for every tool-calling user as soon as the next `transformers` release ships.**
The upstream PR https://github.com/huggingface/transformers/pull/45422 normalizes message inputs by stripping `content=None` before rendering (`None` and absent are semantically identical, and `content=None` is exactly what the OpenAI API returns for tool-call-only messages). That normalization is correct, but it exposes a latent bug in this template: the `tool_calls` branch reads `message['content']` directly, which raises when the key is absent.
Concretely, this code path is hit by **any tool-calling pipeline** (OpenAI-compatible servers, agent frameworks, function-calling demos) that produces assistant messages with `tool_calls` and no textual content. Today most of them happen to pass `content=None` explicitly and get away with it. After the transformers release, all of them break.
### Repro
**Today** (works):
```python
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Math-V2")
tok.apply_chat_template(
[
{"role": "user", "content": "What's the weather in Paris?"},
{"role": "assistant", "content": None, "tool_calls": [{
"type": "function",
"function": {"name": "get_weather", "arguments": '{"city":"Paris"}'},
}]},
],
tokenize=False,
)
# renders correctly
```
**After https://github.com/huggingface/transformers/pull/45422** (same call, same input — `transformers` strips `content=None` before rendering, so the template sees an absent key and crashes):
```
UndefinedError: 'dict object' has no attribute 'content'
```
You can reproduce the post-release behavior today by simply omitting the `content` key.
### The fix
A one-character change: `message['content'] is none` → `message.get('content') is none`. `.get()` returns `None` whether the key is absent or set to `None`, so both cases are handled identically.
Verified against a 14-case regression suite (single-turn, multi-turn, tool flows with/without final answers, multi-system, `</think>` reasoning, unicode, empty content): all cases either render bit-identically to the current template or, for the previously crashing case, render correctly. **Zero regressions.**
---
*Disclaimer: this PR was opened as part of a scan for repos whose `chat_template` is derived from (or copies) the DeepSeek-R1 template, identified by the presence of the buggy substring `message['content'] is none`. The same one-line fix is proposed wherever that pattern appears verbatim. I do not maintain this model, please review before merging.*
- tokenizer_config.json +1 -1
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@@ -31,5 +31,5 @@
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"sp_model_kwargs": {},
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| 32 |
"unk_token": null,
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"tokenizer_class": "LlamaTokenizerFast",
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-
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% if not thinking is defined %}{% set thinking = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false, is_only_sys=false, is_prefix=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}{%- endif %}{% set ns.is_only_sys = true %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<|User|>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- if ns.is_last_user or ns.is_only_sys %}{{'<|Assistant|></think>'}}{%- endif %}{%- set ns.is_last_user = false -%}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message
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}
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"sp_model_kwargs": {},
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"unk_token": null,
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"tokenizer_class": "LlamaTokenizerFast",
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| 34 |
+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% if not thinking is defined %}{% set thinking = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false, is_only_sys=false, is_prefix=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}{%- endif %}{% set ns.is_only_sys = true %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<|User|>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- if ns.is_last_user or ns.is_only_sys %}{{'<|Assistant|></think>'}}{%- endif %}{%- set ns.is_last_user = false -%}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message.get('content') is none %}{{'<|tool▁calls▁begin|><|tool▁call▁begin|>'+ tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}{%- else %}{{message['content'] + '<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'<|tool▁call▁begin|>'+ tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}{%- endif %}{%- endfor %}{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none) %}{%- if ns.is_last_user %}{{'<|Assistant|>'}}{%- if message['prefix'] is defined and message['prefix'] and thinking %}{{'<think>'}}{%- else %}{{'</think>'}}{%- endif %}{%- endif %}{%- if message['prefix'] is defined and message['prefix'] %}{%- set ns.is_prefix = true -%}{%- endif %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{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)[1] -%}{%- endif %}{{content + '<|end▁of▁sentence|>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{{'<|tool▁output▁begin|>' + message['content'] + '<|tool▁output▁end|>'}}{%- endif %}{%- if message['role'] != 'system' %}{% set ns.is_only_sys = false %}{%- endif %}{%- endfor -%}{% if add_generation_prompt and not ns.is_tool%}{% if ns.is_last_user or ns.is_only_sys or not ns.is_prefix %}{{'<|Assistant|>'}}{%- if not thinking %}{{'</think>'}}{%- else %}{{'<think>'}}{%- endif %}{% endif %}{% endif %}"
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}
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