Instructions to use athirdpath/Llama-3-15b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athirdpath/Llama-3-15b-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/Llama-3-15b-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/Llama-3-15b-Instruct")
model = AutoModelForCausalLM.from_pretrained("athirdpath/Llama-3-15b-Instruct")
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

vLLM

How to use athirdpath/Llama-3-15b-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athirdpath/Llama-3-15b-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/Llama-3-15b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/athirdpath/Llama-3-15b-Instruct

SGLang

How to use athirdpath/Llama-3-15b-Instruct 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 "athirdpath/Llama-3-15b-Instruct" \
    --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": "athirdpath/Llama-3-15b-Instruct",
		"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 "athirdpath/Llama-3-15b-Instruct" \
        --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": "athirdpath/Llama-3-15b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use athirdpath/Llama-3-15b-Instruct with Docker Model Runner:
```
docker model run hf.co/athirdpath/Llama-3-15b-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is meant for further finetuning, it is iffy as-is. Made using a new structure I call "ripple merge" that works backwards and forwards through the model.

Other frankenmerge methods were failing at sizes over 11b.

Llama-3-15b-Instruct

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

NousResearch/Meta-Llama-3-8B-Instruct

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [0, 15]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [14, 15]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [13, 14]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [12, 13]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [11, 12]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [10, 11]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [9, 10]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [8, 23]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [21, 22]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [20, 21]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [19, 20]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [18, 19]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [17, 18]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [16, 17]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [15, 16]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [14, 15]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [13, 14]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [12, 13]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [12, 32]

merge_method: passthrough
dtype: float16

Downloads last month: 8

Safetensors

Model size

15B params

Tensor type

F16

Model tree for athirdpath/Llama-3-15b-Instruct

Base model

NousResearch/Meta-Llama-3-8B-Instruct

Finetuned

(21)

this model

Quantizations

1 model