Instructions to use grimjim/llama-3-Nephilim-v2.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/llama-3-Nephilim-v2.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/llama-3-Nephilim-v2.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/llama-3-Nephilim-v2.1-8B")
model = AutoModelForCausalLM.from_pretrained("grimjim/llama-3-Nephilim-v2.1-8B", 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 grimjim/llama-3-Nephilim-v2.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/llama-3-Nephilim-v2.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/llama-3-Nephilim-v2.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/llama-3-Nephilim-v2.1-8B

SGLang

How to use grimjim/llama-3-Nephilim-v2.1-8B 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 "grimjim/llama-3-Nephilim-v2.1-8B" \
    --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": "grimjim/llama-3-Nephilim-v2.1-8B",
		"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 "grimjim/llama-3-Nephilim-v2.1-8B" \
        --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": "grimjim/llama-3-Nephilim-v2.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use grimjim/llama-3-Nephilim-v2.1-8B with Docker Model Runner:
```
docker model run hf.co/grimjim/llama-3-Nephilim-v2.1-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

grimjim/llama-3-Nephilim-v2.1-8B

This repo contains the full bf16 weights of a merge of pre-trained language models created using mergekit.

Select GGUF quants are available here.

Unorthodox contributions to this merge include a biomedical model, a roleplay-focused model, a Japanese language model, and a Korean language model; the original base model was an abliterated model combined with a cybersecurity model. A merger of two recent preference optimization models was also included to shore up reasoning, with the expectation that overall good reasoning and coherence will contribute to narrative flow. Roleplay text generation appears to be easily steered toward emotionally intelligent characters. It should be noted that the vast majority of merge contributions (as measured by weight) were not intended for roleplay.

Tested with 8k context length, temperature=1, and minP=0.01 using Instruct prompts for Llama 3. Example Llama 3 presets can be downloaded here. Specifically, "Llama 3 Lukewarm Presets 8k context.json" corresponds to test settings.

Example context templates variants tested with Llama 3 can be downloaded here; their corresponding Instruct prompts can be downloaded here. Specifically, the two "Llama 3 Instruct Safety.json" files correspond to test settings.

Care should be taken when using this model, as it is possible that harmful outputs may be generated. Given that this model is derivative, responsible use is further mandated by the WhiteRabbitNeo Usage Restrictions Extension to the Llama-3 License. This model is further subject to CC-BY-NC-4.0 by default, meaning that commercial use is restricted, barring an alternative licensing agreement.

Built with Meta Llama 3.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using grimjim/llama-3-Nephilim-v1-8B 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: grimjim/llama-3-Nephilim-v1-8B
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: grimjim/llama-3-Nephilim-v1-8B
  - layer_range: [0, 32]
    model: grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge
    parameters:
      weight: 0.62
  - layer_range: [0, 32]
    model: openlynn/Llama-3-Soliloquy-8B-v2
    parameters:
      weight: 0.03
  - layer_range: [0, 32]
    model: tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: MLP-KTLim/llama-3-Korean-Bllossom-8B
    parameters:
      weight: 0.001
  - layer_range: [0, 32]
    model: grimjim/llama-3-aaditya-OpenBioLLM-8B
    parameters:
      weight: 0.03