Instructions to use arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens", 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 arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens

SGLang

How to use arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens 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 "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens" \
    --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": "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens",
		"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 "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens" \
        --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": "arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens

This model was trained for a single epoch with 1.6B SEC data in Llama-pro style

Model Description

We only trained the newly added blocks as in the Llama pro paper while keeping every other layer frozen.

Finetuned from model: arcee-ai/Mistral-7B-Instruct-v0.2-expanded

Model Sources

Repository: Used huggingface/alignment-handbook - https://github.com/huggingface/alignment-handbook/tree/main/recipes/gpt2-nl/cpt
Paper : LLAMA PRO: Progressive LLaMA with Block Expansion [https://arxiv.org/pdf/2401.02415.pdf]

Uses

Can use with SEC data.

Training Hyperparameters

Trained with p5.48xlarge GPU (8 x 80GB).

# Model arguments
model_name_or_path: arcee-ai/Mistral-7B-Instruct-v0.2-expanded
model_revision: main
torch_dtype: bfloat16

# Data training arguments
dataset_mixer:
  arcee-ai/sec-data-full: 1.0
dataset_splits:
  - train
preprocessing_num_workers: 12

# SFT trainer config
bf16: true
do_eval: False
evaluation_strategy: "no"
gradient_accumulation_steps: 32
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: False
hub_model_id: arcee-ai/mistral-instruct-v2-sec-expanded
hub_strategy: every_save
learning_rate: 2.0e-05
log_level: info
logging_steps: 1  
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 2048
max_steps: -1
num_train_epochs: 1
output_dir: data/mistral-instruct-v2-sec-expanded-new
overwrite_output_dir: true
per_device_eval_batch_size: 1
per_device_train_batch_size: 16
push_to_hub: true
remove_unused_columns: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 100
save_total_limit: 1
seed: 42
warmup_ratio: 0.01

#  ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=8 scripts/run_cpt.py recipes/gpt2-nl/cpt/config_full.yaml

Loss Curves

Downloads last month: 32

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens

Quantizations

2 models

Dataset used to train arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens

Space using arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens 1

Paper for arcee-ai/Mistral-7B-Instruct-v0.2-expanded-sec-1.6B-tokens

LLaMA Pro: Progressive LLaMA with Block Expansion

Paper • 2401.02415 • Published Jan 4, 2024 • 54