Instructions to use vhab10/Llama-3.1-8B-Base-Instruct-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vhab10/Llama-3.1-8B-Base-Instruct-SLERP with Transformers:

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

pipe = pipeline("text-generation", model="vhab10/Llama-3.1-8B-Base-Instruct-SLERP")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vhab10/Llama-3.1-8B-Base-Instruct-SLERP")
model = AutoModelForCausalLM.from_pretrained("vhab10/Llama-3.1-8B-Base-Instruct-SLERP")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vhab10/Llama-3.1-8B-Base-Instruct-SLERP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vhab10/Llama-3.1-8B-Base-Instruct-SLERP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vhab10/Llama-3.1-8B-Base-Instruct-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vhab10/Llama-3.1-8B-Base-Instruct-SLERP

SGLang

How to use vhab10/Llama-3.1-8B-Base-Instruct-SLERP 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 "vhab10/Llama-3.1-8B-Base-Instruct-SLERP" \
    --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": "vhab10/Llama-3.1-8B-Base-Instruct-SLERP",
		"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 "vhab10/Llama-3.1-8B-Base-Instruct-SLERP" \
        --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": "vhab10/Llama-3.1-8B-Base-Instruct-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vhab10/Llama-3.1-8B-Base-Instruct-SLERP with Docker Model Runner:
```
docker model run hf.co/vhab10/Llama-3.1-8B-Base-Instruct-SLERP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - model: meta-llama/Meta-Llama-3.1-8B
    layer_range:
    - 0
    - 32
  - model: meta-llama/Meta-Llama-3.1-8B-Instruct
    layer_range:
    - 0
    - 32
merge_method: slerp
base_model: meta-llama/Meta-Llama-3.1-8B
parameters:
  t:
  - filter: self_attn
    value:
    - 0
    - 0.5
    - 0.3
    - 0.7
    - 1
  - filter: mlp
    value:
    - 1
    - 0.5
    - 0.7
    - 0.3
    - 0
  - value: 0.5
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.02
IFEval (0-Shot)	29.07
BBH (3-Shot)	29.93
MATH Lvl 5 (4-Shot)	10.50
GPQA (0-shot)	6.15
MuSR (0-shot)	9.37
MMLU-PRO (5-shot)	29.12

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for vhab10/Llama-3.1-8B-Base-Instruct-SLERP

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1439)

this model

Quantizations

1 model

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

29.070
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

29.930
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

10.500
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

6.150
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

9.370
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

29.120