Instructions to use TRAC-MTRY/traclm-v2-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TRAC-MTRY/traclm-v2-7b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="TRAC-MTRY/traclm-v2-7b-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-v2-7b-instruct")
model = AutoModelForCausalLM.from_pretrained("TRAC-MTRY/traclm-v2-7b-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 Settings

vLLM

How to use TRAC-MTRY/traclm-v2-7b-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-v2-7b-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-v2-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TRAC-MTRY/traclm-v2-7b-instruct

SGLang

How to use TRAC-MTRY/traclm-v2-7b-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-v2-7b-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-v2-7b-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-v2-7b-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-v2-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TRAC-MTRY/traclm-v2-7b-instruct with Docker Model Runner:
```
docker model run hf.co/TRAC-MTRY/traclm-v2-7b-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-v2-7b-instruct

An instruction-tuned version of TRAC-MTRY/traclm-v2-7b-base created by further finetuning on the popular "Alpaca" distillation of GPT4 prompts/responses.

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 - Monterey, Army Futures Command
License: Llama-2 Community License
Model Type: LlamaForCausalLM
Finetuned from model: TRAC-MTRY/traclm-v2-7b-base

Model Sources [optional]

Paper: TBP
Demo: TBP

Downstream Use

This model is instruction-tuned, and thus is more capable of following user instructions than the raw '-base' counterpart. However, this model is still capable of extreme hallucination, and thus is only suitable for research purposes.

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 language models. Direct application to downstream military tasks is out of scope.

Prompt Format

This model was fine-tuned with the alpaca 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.

Standard Alpaca Format:

### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n### Response:\n "

Input Field Variant:

### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n###Input:\n{input}\n\n### Response:\n "

Training Details

Training Data

teknium/GPT4-LLM-Cleaned

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 with NPS's Hamming HPC Center. The compute node contained 8x NVIDIA A40 GPUs.

Training Hyperparameters

base_model: TRAC-MTRY/traclm-v2-7b-base
base_model_config: TRAC-MTRY/traclm-v2-7b-base
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
sequence_len: 4096
pad_to_sequence_len: true
gradient_accumulation_steps: 1
micro_batch_size: 4
eval_batch_size: 4
num_epochs: 5
lr_scheduler: cosine
learning_rate: 0.00003
bf16: true
gradient_checkpointing: true
flash_attention: true
warmup_steps: 50
lr_quadratic_warmup: true
special_tokens: {bos_token: "<s>", eos_token: "</s>", unk_token: "<unk>"}

DeepSpeed Configuration

{
    "zero_optimization": {
      "stage": 2,
      "offload_optimizer": {
        "device": "cpu"
      },
      "contiguous_gradients": true,
      "overlap_comm": true
    },
    "bf16": {
      "enabled": "auto"
    },
    "fp16": {
      "enabled": "auto",
      "auto_cast": false,
      "loss_scale": 0,
      "initial_scale_power": 32,
      "loss_scale_window": 1000,
      "hysteresis": 2,
      "min_loss_scale": 1
    },
    "optimizer": {
      "type": "AdamW",
      "params": {
        "lr": "auto",
        "betas": [
          0.9,
          0.999
        ],
        "eps": 1e-8,
        "weight_decay": "auto"
      }
    },
    "scheduler": {
      "type": "WarmupDecayLR",
      "params": {
        "warmup_min_lr": "auto",
        "warmup_max_lr": "auto",
        "warmup_num_steps": "auto",
        "total_num_steps": "auto"
      }
    },
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "wall_clock_breakdown": false
  }

Model Card Contact

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

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F32

TRAC-MTRY
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traclm-v2-7b-instruct