Instructions to use gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES with Transformers:

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

pipe = pipeline("text-generation", model="gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES")
model = AutoModelForCausalLM.from_pretrained("gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES")
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]:]))

Inference
Local Apps Settings

vLLM

How to use gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES

SGLang

How to use gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES 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 "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES" \
    --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": "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES",
		"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 "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES" \
        --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": "gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES with Docker Model Runner:
```
docker model run hf.co/gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
```

Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES / README.md

leaderboard-pr-bot

Adding Evaluation Results

89e96b7 verified almost 2 years ago

4.68 kB

license: llama3.1
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
  - meta-llama/Meta-Llama-3.1-8B
  - meta-llama/Meta-Llama-3.1-8B-Instruct
model-index:
  - name: Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
    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: 45.51
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          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: 28.91
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          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: 11.63
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          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: 2.24
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          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: 6.59
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          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: 29.76
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gaverfraxz/Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES
          name: Open LLM Leaderboard

Meta-Llama-3.1-8B-Instruct-HalfAbliterated-TIES

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

Merge Details

Merge Method

This model was merged using the TIES merge method using meta-llama/Meta-Llama-3.1-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Meta-Llama-3.1-8B
chat_template: auto
dtype: float16
merge_method: ties
models:
- model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
  parameters:
    density: 0.5
    weight: 0.5
- model: meta-llama/Meta-Llama-3.1-8B-Instruct
  parameters:
    density: 0.5
    weight: 0.5
parameters:
  int8_mask: true
  normalize: false

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	20.77
IFEval (0-Shot)	45.51
BBH (3-Shot)	28.91
MATH Lvl 5 (4-Shot)	11.63
GPQA (0-shot)	2.24
MuSR (0-shot)	6.59
MMLU-PRO (5-shot)	29.76