Instructions to use YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco with Transformers:

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

pipe = pipeline("text-generation", model="YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco")
model = AutoModelForCausalLM.from_pretrained("YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco

SGLang

How to use YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco 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 "YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco" \
    --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": "YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco",
		"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 "YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco" \
        --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": "YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco with Docker Model Runner:
```
docker model run hf.co/YeonwooSung/llama-3.1-8B-Galore-openassistant-guanaco
```

llama-3.1-8B-Galore-openassistant-guanaco / README.md

Yeonwoo Sung

leaderboard-pr-bot

Adding Evaluation Results (#1)

76c8a1a verified almost 2 years ago

preview code

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metadata

library_name: transformers
tags:
  - trl
  - sft
model-index:
  - name: llama-3.1-8B-Galore-openassistant-guanaco
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 26.35
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 31.44
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 4.83
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 6.71
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 14.58
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 24.52
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BlackBeenie/llama-3.1-8B-Galore-openassistant-guanaco
          name: Open LLM Leaderboard

Model Card for Model ID

Model Details

Training Details

Training Data

timdettmers/openassistant-guanaco

Training Procedure

Trained with SFTTrainer with Galore quantization.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	18.07
IFEval (0-Shot)	26.35
BBH (3-Shot)	31.44
MATH Lvl 5 (4-Shot)	4.83
GPQA (0-shot)	6.71
MuSR (0-shot)	14.58
MMLU-PRO (5-shot)	24.52