Instructions to use seanmemery/ShaderLLM-CodeLlama-13b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seanmemery/ShaderLLM-CodeLlama-13b-it with Transformers:

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

pipe = pipeline("text-generation", model="seanmemery/ShaderLLM-CodeLlama-13b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("seanmemery/ShaderLLM-CodeLlama-13b-it")
model = AutoModelForCausalLM.from_pretrained("seanmemery/ShaderLLM-CodeLlama-13b-it", 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 seanmemery/ShaderLLM-CodeLlama-13b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seanmemery/ShaderLLM-CodeLlama-13b-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seanmemery/ShaderLLM-CodeLlama-13b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/seanmemery/ShaderLLM-CodeLlama-13b-it

SGLang

How to use seanmemery/ShaderLLM-CodeLlama-13b-it 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 "seanmemery/ShaderLLM-CodeLlama-13b-it" \
    --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": "seanmemery/ShaderLLM-CodeLlama-13b-it",
		"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 "seanmemery/ShaderLLM-CodeLlama-13b-it" \
        --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": "seanmemery/ShaderLLM-CodeLlama-13b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use seanmemery/ShaderLLM-CodeLlama-13b-it with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for seanmemery/ShaderLLM-CodeLlama-13b-it to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for seanmemery/ShaderLLM-CodeLlama-13b-it to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for seanmemery/ShaderLLM-CodeLlama-13b-it to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="seanmemery/ShaderLLM-CodeLlama-13b-it",
    max_seq_length=2048,
)

Docker Model Runner
How to use seanmemery/ShaderLLM-CodeLlama-13b-it with Docker Model Runner:
```
docker model run hf.co/seanmemery/ShaderLLM-CodeLlama-13b-it
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ShaderLLM-CodeLlama-13b-it

This model is a fine-tuned version of codellama/CodeLlama-13b-Instruct-hf on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.7616

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0025
train_batch_size: 4
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.5616	0.24	1000	1.2367
1.0239	0.49	2000	1.0618
0.9445	0.73	3000	0.9659
1.0534	0.98	4000	0.8703
0.653	1.22	5000	0.8155
0.6389	1.46	6000	0.7856
0.6987	1.71	7000	0.7670
0.6333	1.95	8000	0.7616

Framework versions

Transformers 4.39.3
Pytorch 2.2.2
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 4

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for seanmemery/ShaderLLM-CodeLlama-13b-it

Base model

codellama/CodeLlama-13b-Instruct-hf

Finetuned

(25)

this model

Quantizations

1 model