Instructions to use TURKCELL/Turkcell-LLM-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TURKCELL/Turkcell-LLM-7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="TURKCELL/Turkcell-LLM-7b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
model = AutoModelForCausalLM.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
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 TURKCELL/Turkcell-LLM-7b-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TURKCELL/Turkcell-LLM-7b-v1

SGLang

How to use TURKCELL/Turkcell-LLM-7b-v1 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 "TURKCELL/Turkcell-LLM-7b-v1" \
    --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": "TURKCELL/Turkcell-LLM-7b-v1",
		"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 "TURKCELL/Turkcell-LLM-7b-v1" \
        --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": "TURKCELL/Turkcell-LLM-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TURKCELL/Turkcell-LLM-7b-v1 with Docker Model Runner:
```
docker model run hf.co/TURKCELL/Turkcell-LLM-7b-v1
```

Add 4-bit quantization and automatic device mapping for improved performance.

by notbdq - opened Apr 6, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+41

-0

Files changed (1) hide show

README.md +41 -0

README.md CHANGED Viewed

@@ -63,3 +63,44 @@ generated_ids = model.generate(model_inputs,
 decoded = tokenizer.batch_decode(generated_ids)
 print(decoded[0])

 decoded = tokenizer.batch_decode(generated_ids)
 print(decoded[0])
+```
+# 4-bit Quantized Inference
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+quantization_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.float16 # or torch.bfloat16
+)
+model = AutoModelForCausalLM.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1", device_map="auto", quantization_config=quantization_config)
+tokenizer = AutoTokenizer.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
+messages = [
+    {"role": "user", "content": "Türkiye'nin başkenti neresidir?"},
+]
+encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
+eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0]
+device = "cuda"
+model_inputs = encodeds.to(device)
+generated_ids = model.generate(model_inputs,
+                               max_new_tokens=1024,
+                               do_sample=True,
+                               eos_token_id=eos_token)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```