Instructions to use jahyungu/deepseek-math-7b-instruct-Humanities with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jahyungu/deepseek-math-7b-instruct-Humanities with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-math-7b-instruct")
model = PeftModel.from_pretrained(base_model, "jahyungu/deepseek-math-7b-instruct-Humanities")

Transformers

How to use jahyungu/deepseek-math-7b-instruct-Humanities with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jahyungu/deepseek-math-7b-instruct-Humanities")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jahyungu/deepseek-math-7b-instruct-Humanities", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jahyungu/deepseek-math-7b-instruct-Humanities with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jahyungu/deepseek-math-7b-instruct-Humanities"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jahyungu/deepseek-math-7b-instruct-Humanities",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jahyungu/deepseek-math-7b-instruct-Humanities

SGLang

How to use jahyungu/deepseek-math-7b-instruct-Humanities 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 "jahyungu/deepseek-math-7b-instruct-Humanities" \
    --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": "jahyungu/deepseek-math-7b-instruct-Humanities",
		"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 "jahyungu/deepseek-math-7b-instruct-Humanities" \
        --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": "jahyungu/deepseek-math-7b-instruct-Humanities",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jahyungu/deepseek-math-7b-instruct-Humanities with Docker Model Runner:
```
docker model run hf.co/jahyungu/deepseek-math-7b-instruct-Humanities
```

jahyungu commited on Feb 25

Commit

8c064ff

verified ·

1 Parent(s): 998b011

Model save

Browse files

Files changed (4) hide show

README.md +63 -0
chat_template.jinja +5 -0
tokenizer.json +0 -0
tokenizer_config.json +13 -0

README.md ADDED Viewed

	@@ -0,0 +1,63 @@

+---
+library_name: peft
+license: other
+base_model: deepseek-ai/deepseek-math-7b-instruct
+tags:
+- base_model:adapter:deepseek-ai/deepseek-math-7b-instruct
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: deepseek-math-7b-instruct-Humanities
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# deepseek-math-7b-instruct-Humanities
+This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-instruct](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- total_eval_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 0.03
+- num_epochs: 2
+### Training results
+### Framework versions
+- PEFT 0.18.1
+- Transformers 5.2.0
+- Pytorch 2.10.0+cu130
+- Datasets 4.5.0
+- Tokenizers 0.22.2

chat_template.jinja ADDED Viewed

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+{% 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'] + '
+' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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+{
+  "backend": "tokenizers",
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "is_local": false,
+  "legacy": true,
+  "model_max_length": 4096,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null
+}