Text Generation
Transformers
Safetensors
qwen2
mergekit
Merge
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged")
model = AutoModelForCausalLM.from_pretrained("realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))Quick Links
FuseO1-DeepSeekR1-Math-Code
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the sce merge method using Qwen/Qwen2.5-14B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
# Pivot model
- model: Qwen/Qwen2.5-14B
# Target models
- model: realYinkaIyiola/Deepseek-R1-Distill-14B-Math
- model: realYinkaIyiola/Deepseek-R1-Distill-14B-Code
merge_method: sce
base_model: Qwen/Qwen2.5-14B
parameters:
select_topk: 1.0
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)