Instructions to use gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge with Transformers:

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

pipe = pipeline("text-generation", model="gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge")
model = AutoModelForCausalLM.from_pretrained("gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge

SGLang

How to use gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge 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 "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge" \
    --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": "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge",
		"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 "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge" \
        --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": "gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge with Docker Model Runner:
```
docker model run hf.co/gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge
```

fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-mmot_merge / mergekit_config.yml

gsjang

Automated upload: mergekit custom merges (otter_merge/skt/nkcm)

b6c8a06 verified about 1 year ago

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	dtype: bfloat16
	tokenizer:
	source: union
	merge_method: mmot_merge
	base_model: meta-llama/Meta-Llama-3-8B-Instruct
	models:
	- model: meta-llama/Meta-Llama-3-8B-Instruct
	parameters: {}
	- model: PartAI/Dorna-Llama3-8B-Instruct
	parameters: {}
	parameters:
	align: true
	eps: 0.05
	iters: 30
	gamma: 1.0
	eta: 5.0
	alpha_cap: 0.8
	ot_cap: 512
	group_size: 512
	cpu_align: false
	write_readme: README.md