Instructions to use jjjlimaus/round10-chrisdan-fitleech-mean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jjjlimaus/round10-chrisdan-fitleech-mean with Transformers:

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

pipe = pipeline("text-generation", model="jjjlimaus/round10-chrisdan-fitleech-mean")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jjjlimaus/round10-chrisdan-fitleech-mean", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jjjlimaus/round10-chrisdan-fitleech-mean with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jjjlimaus/round10-chrisdan-fitleech-mean"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jjjlimaus/round10-chrisdan-fitleech-mean",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jjjlimaus/round10-chrisdan-fitleech-mean

SGLang

How to use jjjlimaus/round10-chrisdan-fitleech-mean 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 "jjjlimaus/round10-chrisdan-fitleech-mean" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jjjlimaus/round10-chrisdan-fitleech-mean",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "jjjlimaus/round10-chrisdan-fitleech-mean" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jjjlimaus/round10-chrisdan-fitleech-mean",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jjjlimaus/round10-chrisdan-fitleech-mean with Docker Model Runner:
```
docker model run hf.co/jjjlimaus/round10-chrisdan-fitleech-mean
```

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round10-chrisdan-fitleech-mean

Element-wise mean merge (bf16) of:

Formula: 0.5*chrisdan775/round10@e30129f5ff33 + 0.5*fitleech/sn38-model-2020-1@d5cc8851e918

Common tensors averaged 50/50. Four model.extra_mlps.* tensors present only in fitleech were kept from that parent. Config/tokenizer from fitleech.

Public + gated=manual.

Downloads last month: 4

Safetensors

Model size

2B params

Tensor type

BF16