Instructions to use CerebrumTech/cere-llama-3-8b-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CerebrumTech/cere-llama-3-8b-tr with Transformers:

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

pipe = pipeline("text-generation", model="CerebrumTech/cere-llama-3-8b-tr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CerebrumTech/cere-llama-3-8b-tr")
model = AutoModelForCausalLM.from_pretrained("CerebrumTech/cere-llama-3-8b-tr")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CerebrumTech/cere-llama-3-8b-tr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CerebrumTech/cere-llama-3-8b-tr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CerebrumTech/cere-llama-3-8b-tr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CerebrumTech/cere-llama-3-8b-tr

SGLang

How to use CerebrumTech/cere-llama-3-8b-tr 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 "CerebrumTech/cere-llama-3-8b-tr" \
    --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": "CerebrumTech/cere-llama-3-8b-tr",
		"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 "CerebrumTech/cere-llama-3-8b-tr" \
        --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": "CerebrumTech/cere-llama-3-8b-tr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CerebrumTech/cere-llama-3-8b-tr with Docker Model Runner:
```
docker model run hf.co/CerebrumTech/cere-llama-3-8b-tr
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CEREBRUM LLM

CERE-LLMA-3-8b-TR

This model is an fine-tuned version of a Llama3 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner.

Model Details

Base Model: LLMA 3 8B based LLM
Tokenizer Extension: Specifically extended for Turkish
Training Dataset: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
Training Method: Initially with DORA, followed by fine-tuning with LORA

Benchmark Results

Winogrande_tr: 56.16
TruthfulQA_tr_v0.2: 47.46
Mmlu_tr_v0.2: 46.46
HellaSwag_tr_v0.2: 48.87
GSM8k_tr_v0.2: 25.43
Arc_tr_v0.2: 41.97

Usage Examples


from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Cerebrum/cere-llama-3-8b-tr",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3-8b-tr")

prompt = "Python'da ekrana 'Merhaba Dünya' nasıl yazılır?"
messages = [
    {"role": "system", "content": "Sen, Cerebrum Tech tarafından üretilen ve verilen talimatları takip ederek en iyi cevabı üretmeye çalışan yardımcı bir yapay zekasın."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    temperature=0.3,
    top_k=50,
    top_p=0.9,
    max_new_tokens=512,
    repetition_penalty=1,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

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Safetensors

Model size

8B params

Tensor type

F16

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Quantizations

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Model Details

Benchmark Results

Usage Examples

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