Instructions to use TRAC-MTRY/traclm-v3-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TRAC-MTRY/traclm-v3-7b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="TRAC-MTRY/traclm-v3-7b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TRAC-MTRY/traclm-v3-7b-instruct")
model = AutoModelForCausalLM.from_pretrained("TRAC-MTRY/traclm-v3-7b-instruct")
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

vLLM

How to use TRAC-MTRY/traclm-v3-7b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TRAC-MTRY/traclm-v3-7b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TRAC-MTRY/traclm-v3-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TRAC-MTRY/traclm-v3-7b-instruct

SGLang

How to use TRAC-MTRY/traclm-v3-7b-instruct 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 "TRAC-MTRY/traclm-v3-7b-instruct" \
    --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": "TRAC-MTRY/traclm-v3-7b-instruct",
		"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 "TRAC-MTRY/traclm-v3-7b-instruct" \
        --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": "TRAC-MTRY/traclm-v3-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TRAC-MTRY/traclm-v3-7b-instruct with Docker Model Runner:
```
docker model run hf.co/TRAC-MTRY/traclm-v3-7b-instruct
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Model Card for traclm-v3-7b-instruct

An Army domain finetune of mistralai/Mistral-7B-v0.1 created by finetuning on a merger of domain-specific and general-purpose instruction-tuning datasets.

Model Details

Model Description

This model is a research project aimed at exploring whether a pretrained LLM can acquire tangible domain-specific knowledge about the Army domain.

Developed by: The Research and Analysis Center, Army Futures Command, U.S. Army
License: MIT
Model Type: MistralForCausalLM
Finetuned from model: mistralai/Mistral-7B-v0.1

Available Quantizations (for running on low-resource hardware):

Downstream Use

This model is instruction-tuned, and is thus more capable of following user instructions than its corresponding base version. However, this model is still capable of extreme hallucination, so all outputs should be verified by end users.

Out-of-Scope Use

The creation of this model constitutes academic research in partnership with the Naval Postgraduate School. The purpose of this research is to inform future DoD experimentation regarding the development and application of domain-specific large language models. Experiments involving direct application of this model to downstream military tasks are encouraged, but extreme caution should be exercised before productionalization.

Prompt Format

This model was fine-tuned with the chatml prompt format. It is highly recommended that you use the same format for any interactions with the model. Failure to do so will degrade performance significantly.

ChatML Format:

<|im_start|>system 
Provide some context and/or instructions to the model.
<|im_end|> 
<|im_start|>user 
The user’s message goes here
<|im_end|> 
<|im_start|>assistant

The ChatML format can easily be applied to text you plan to process with the model using the chat_template included in the tokenizer. Read here for additional information.

Training Details

Training Data

This model was trained on a shuffled merger of the following datasets:

General Purpose Instruction Tuning: Open-Orca/SlimOrca-Dedup
Domain Specific Instruction Tuning: TRAC-MTRY/traclm-v3-data (TBP)

Training Procedure

The model was trained using Open Access AI Collective's Axolotl framework and Microsoft's DeepSpeed framework for model/data parallelism.

Training Hardware

Training was conducted on a single compute node 4x NVIDIA A100 GPUs.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-06
train_batch_size: 4
eval_batch_size: 4
seed: 28
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 19
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.5726	0.0	1	1.6102
1.2333	0.2	510	1.2477
1.1485	0.4	1020	1.2010
1.106	0.6	1530	1.1687
1.1772	0.8	2040	1.1419
1.1567	1.0	2550	1.1190
1.0359	1.19	3060	1.1130
0.945	1.39	3570	1.0977
0.9365	1.59	4080	1.0831
0.9334	1.79	4590	1.0721
0.8913	1.99	5100	1.0627
0.804	2.18	5610	1.0922
0.7892	2.38	6120	1.0888
0.7757	2.58	6630	1.0873
0.7797	2.78	7140	1.0864

Framework versions

Transformers 4.38.2
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.0

Model Card Contact

MAJ Daniel C. Ruiz (daniel.ruiz@nps.edu)

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

TRAC-MTRY
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traclm-v3-7b-instruct