Instructions to use sequelbox/Llama3.1-70B-PlumChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sequelbox/Llama3.1-70B-PlumChat with Transformers:

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

pipe = pipeline("text-generation", model="sequelbox/Llama3.1-70B-PlumChat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Llama3.1-70B-PlumChat")
model = AutoModelForCausalLM.from_pretrained("sequelbox/Llama3.1-70B-PlumChat", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sequelbox/Llama3.1-70B-PlumChat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sequelbox/Llama3.1-70B-PlumChat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sequelbox/Llama3.1-70B-PlumChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sequelbox/Llama3.1-70B-PlumChat

SGLang

How to use sequelbox/Llama3.1-70B-PlumChat 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 "sequelbox/Llama3.1-70B-PlumChat" \
    --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": "sequelbox/Llama3.1-70B-PlumChat",
		"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 "sequelbox/Llama3.1-70B-PlumChat" \
        --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": "sequelbox/Llama3.1-70B-PlumChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sequelbox/Llama3.1-70B-PlumChat with Docker Model Runner:
```
docker model run hf.co/sequelbox/Llama3.1-70B-PlumChat
```

Llama3.1-70B-PlumChat / README.md

sequelbox

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	---
	base_model:
	- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
	- meta-llama/Llama-3.1-70B-Instruct
	- ValiantLabs/Llama3.1-70B-ShiningValiant2
	language:
	- en
	library_name: transformers
	license: llama3.1
	tags:
	- llama
	- llama3.1
	- llama3
	- meta
	- 70b
	- science
	- physics
	- biology
	- chemistry
	- compsci
	- computer-science
	- engineering
	- logic
	- rationality
	- advanced
	- expert
	- technical
	- conversational
	- chat
	- instruct
	- mergekit
	- merge
	pipeline_tag: text-generation
	model_type: llama
	model-index:
	- name: sequelbox/Llama3.1-70B-PlumChat
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-Shot)
	type: Winogrande
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 85.00
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: ARC Challenge (25-Shot)
	type: arc-challenge
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 67.41
	name: normalized accuracy
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU College Biology (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 93.75
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU High School Biology (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 91.94
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU Conceptual Physics (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 82.13
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU College Physics (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 60.78
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU High School Physics (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 62.25
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU College Chemistry (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 56.00
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU High School Chemistry (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 73.40
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU Astronomy (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 89.47
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU College Computer Science (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 64.00
	name: acc
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU High School Computer Science (5-Shot)
	type: MMLU
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 90.00
	name: acc
	- 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: 56.16
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	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: 52.81
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	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: 29.98
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	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: 18.79
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	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: 20.14
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	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: 46.26
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumChat
	name: Open LLM Leaderboard

	---
	# PlumChat 70b

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details

	Shining Valiant 2 + Nemotron for high quality general chat, science-instruct, and complex query performance.

	### Merge Method

	This model was merged using the della merge method using [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) as a base.

	### Models Merged

	The following models were included in the merge:
	* [nvidia/Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF)
	* [ValiantLabs/Llama3.1-70B-ShiningValiant2](https://huggingface.co/ValiantLabs/Llama3.1-70B-ShiningValiant2)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	merge_method: della
	dtype: bfloat16
	parameters:
	normalize: true
	models:
	- model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
	parameters:
	density: 0.5
	weight: 0.3
	- model: ValiantLabs/Llama3.1-70B-ShiningValiant2
	parameters:
	density: 0.5
	weight: 0.25
	base_model: meta-llama/Llama-3.1-70B-Instruct

	```