Instructions to use umarigan/TURKCELL-LLM-7B-openhermes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umarigan/TURKCELL-LLM-7B-openhermes with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("umarigan/TURKCELL-LLM-7B-openhermes", set_active=True)

Transformers

How to use umarigan/TURKCELL-LLM-7B-openhermes with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umarigan/TURKCELL-LLM-7B-openhermes")
model = AutoModelForCausalLM.from_pretrained("umarigan/TURKCELL-LLM-7B-openhermes")
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

vLLM

How to use umarigan/TURKCELL-LLM-7B-openhermes with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/umarigan/TURKCELL-LLM-7B-openhermes

SGLang

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

Unsloth Studio new

How to use umarigan/TURKCELL-LLM-7B-openhermes 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 umarigan/TURKCELL-LLM-7B-openhermes 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 umarigan/TURKCELL-LLM-7B-openhermes to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for umarigan/TURKCELL-LLM-7B-openhermes to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="umarigan/TURKCELL-LLM-7B-openhermes",
    max_seq_length=2048,
)

Docker Model Runner
How to use umarigan/TURKCELL-LLM-7B-openhermes with Docker Model Runner:
```
docker model run hf.co/umarigan/TURKCELL-LLM-7B-openhermes
```

Uploaded model

Developed by: umarigan
License: apache-2.0
Finetuned from model : TURKCELL/Turkcell-LLM-7b-v1

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

This model is fine-tuned version of Trendyol chat v1.0 on openhermes dataset. The dataset translated from English to Turkish lanuage and trained by unsloth's mistral scripts. Usage:

# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation",
                model="umarigan/TURKCELL-LLM-7B-openhermes",
                #use_flash_attention_2=True
                )
# Generate text
q = "Weng bebek bakıcılığından saatte 12 dolar kazanıyor. Dün sadece 50 dakika bebek bakıcılığı yaptı. Ne kadar kazandı?"
sequences_rlhf = pipe(
    q,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=2048,
)
sequences_rlhf[0]['generated_text']

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Model tree for umarigan/TURKCELL-LLM-7B-openhermes

Base model

TURKCELL/Turkcell-LLM-7b-v1

Adapter

(1)

this model

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2 models

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/

TURKCELL-LLM-7B-openhermes

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Model tree for umarigan/TURKCELL-LLM-7B-openhermes

Dataset used to train umarigan/TURKCELL-LLM-7B-openhermes