Text Generation
Transformers
Safetensors
Turkish
English
llama
conversational
text-generation-inference
Instructions to use Trendyol/Trendyol-LLM-7b-chat-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trendyol/Trendyol-LLM-7b-chat-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Trendyol/Trendyol-LLM-7b-chat-v0.1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Trendyol/Trendyol-LLM-7b-chat-v0.1") model = AutoModelForMultimodalLM.from_pretrained("Trendyol/Trendyol-LLM-7b-chat-v0.1") 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 Trendyol/Trendyol-LLM-7b-chat-v0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Trendyol/Trendyol-LLM-7b-chat-v0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Trendyol/Trendyol-LLM-7b-chat-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Trendyol/Trendyol-LLM-7b-chat-v0.1
- SGLang
How to use Trendyol/Trendyol-LLM-7b-chat-v0.1 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 "Trendyol/Trendyol-LLM-7b-chat-v0.1" \ --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": "Trendyol/Trendyol-LLM-7b-chat-v0.1", "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 "Trendyol/Trendyol-LLM-7b-chat-v0.1" \ --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": "Trendyol/Trendyol-LLM-7b-chat-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Trendyol/Trendyol-LLM-7b-chat-v0.1 with Docker Model Runner:
docker model run hf.co/Trendyol/Trendyol-LLM-7b-chat-v0.1
Added example for chat template
Browse files
README.md
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@@ -77,8 +77,32 @@ def generate_output(user_query, sys_prompt=DEFAULT_SYSTEM_PROMPT):
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user_query = "Türkiye'de kaç il var?"
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response = generate_output(user_query)
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```
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## Limitations, Risks, Bias, and Ethical Considerations
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### Limitations and Known Biases
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user_query = "Türkiye'de kaç il var?"
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response = generate_output(user_query)
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print(response)
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```
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with chat template:
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```python
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pipe = pipeline("conversational",
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model=model,
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tokenizer=tokenizer,
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device_map="auto",
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max_new_tokens=1024,
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repetition_penalty=1.1
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)
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messages = [
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{
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"role": "system",
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"content": "Sen yardımsever bir chatbotsun. Sana verilen diyalog akışına dikkat ederek diyaloğu devam ettir.",
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},
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{"role": "user", "content": "Türkiye'de kaç il var?"}
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]
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outputs = pipe(messages, **sampling_params)
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print(outputs)
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```
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## Limitations, Risks, Bias, and Ethical Considerations
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### Limitations and Known Biases
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