Text Generation
Transformers
Safetensors
llama
mergekit
Merge
conversational
text-generation-inference
How to use from
SGLangUse 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 "Alelcv27/Llama3.1-8B-Arcee-Math-Code-v1" \
--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": "Alelcv27/Llama3.1-8B-Arcee-Math-Code-v1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Quick Links
Llama3.1-8B-Arcee-Math-Code-v1
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Arcee Fusion merge method using Alelcv27/Llama3.1-8B-Math-v3 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: Alelcv27/Llama3.1-8B-Math-v3
dtype: bfloat16
merge_method: arcee_fusion
modules:
default:
slices:
- sources:
- layer_range: [0, 32]
model: Alelcv27/Llama3.1-8B-Math-v3
- layer_range: [0, 32]
model: Alelcv27/Llama3.1-8B-Code
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Alelcv27/Llama3.1-8B-Arcee-Math-Code-v1" \ --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": "Alelcv27/Llama3.1-8B-Arcee-Math-Code-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'