Text Generation
Transformers
Safetensors
English
llama
llama3.1
llama3
meta
70b
science
physics
biology
chemistry
compsci
computer-science
engineering
logic
rationality
advanced
expert
technical
conversational
chat
instruct
mergekit
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use sequelbox/Llama3.1-70B-PlumChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
metadata
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
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
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: 73.4
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
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
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.
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 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
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