Instructions to use trans-realities-lab/Parser-codellama-7b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trans-realities-lab/Parser-codellama-7b-v0 with Transformers:

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

pipe = pipeline("text-generation", model="trans-realities-lab/Parser-codellama-7b-v0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trans-realities-lab/Parser-codellama-7b-v0")
model = AutoModelForCausalLM.from_pretrained("trans-realities-lab/Parser-codellama-7b-v0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use trans-realities-lab/Parser-codellama-7b-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trans-realities-lab/Parser-codellama-7b-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trans-realities-lab/Parser-codellama-7b-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/trans-realities-lab/Parser-codellama-7b-v0

SGLang

How to use trans-realities-lab/Parser-codellama-7b-v0 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 "trans-realities-lab/Parser-codellama-7b-v0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trans-realities-lab/Parser-codellama-7b-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "trans-realities-lab/Parser-codellama-7b-v0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trans-realities-lab/Parser-codellama-7b-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use trans-realities-lab/Parser-codellama-7b-v0 with Docker Model Runner:
```
docker model run hf.co/trans-realities-lab/Parser-codellama-7b-v0
```

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Parser-CodeLlama-7B v0

A fine-tuned CodeLlama-7B model that converts natural language commands into structured Unreal Engine JSON property modifications.

Model Description

This model was trained to interpret natural language commands in the context of Unreal Engine development and output structured JSON that can be directly used to modify object properties.

Capabilities

Movement commands: "Move the box forward 100 units"
Rotation commands: "Rotate 90 degrees to the right"
Scale commands: "Make this twice as big"
Spawn commands: "Create a sphere here"
Delete commands: "Remove this object"

Performance

Metric	Score
Structure Accuracy	100%
Value Accuracy	~90%
JSON Validity	100%

Example

Input:

User Command: "Rotate the cube 90 degrees"
Object State: "Cube"
{"RelativeRotation": "(Pitch=0.0,Yaw=45.0,Roll=0.0)"}

Output:

[{
  "Action": "Edit",
  "PropertyName": "RelativeRotation",
  "PropertyType": "StructProperty",
  "Value": "(Pitch=0.0,Yaw=135.0,Roll=0.0)"
}]

Technical Details


Base Model	meta-llama/CodeLlama-7b-hf
Fine-tuning Method	LoRA
Training Examples	5,000
LoRA Rank	16
Training Iterations	800
Final Loss	0.096

Requirements

Platform	Library	Memory
Apple Silicon	`mlx-lm`	~14GB
NVIDIA GPU	`transformers` + `torch`	~14GB VRAM

Important Notes

System prompt required: This model requires a specific system prompt to achieve optimal performance. Contact the maintainers for documentation.
Output processing: Model output may contain special tokens that need to be stripped before JSON parsing.

License

This model is subject to the Llama 3.2 Community License and additional usage restrictions.

Contact

For access requests or questions, please submit a request through the Hugging Face interface.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for trans-realities-lab/Parser-codellama-7b-v0

Base model

meta-llama/CodeLlama-7b-hf

Finetuned

(29)

this model

Collection including trans-realities-lab/Parser-codellama-7b-v0

Holodeck Parser

Collection

Fine-tuned models for natural language → Unreal Engine command parsing • 3 items • Updated about 5 hours ago