Text Generation
Transformers
Turkish
English
chat-template
jinja2
prompt-format
pharmacy
tool-calling
rag
conversational
Instructions to use menesnas/ChatTemplate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use menesnas/ChatTemplate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="menesnas/ChatTemplate") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("menesnas/ChatTemplate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use menesnas/ChatTemplate with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "menesnas/ChatTemplate" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "menesnas/ChatTemplate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/menesnas/ChatTemplate
- SGLang
How to use menesnas/ChatTemplate 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 "menesnas/ChatTemplate" \ --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": "menesnas/ChatTemplate", "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 "menesnas/ChatTemplate" \ --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": "menesnas/ChatTemplate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use menesnas/ChatTemplate with Docker Model Runner:
docker model run hf.co/menesnas/ChatTemplate
File size: 4,449 Bytes
5a4ba7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import json
import jinja2
def tojson_filter(val, indent=None):
if indent is not None:
return json.dumps(val, ensure_ascii=False, indent=indent)
return json.dumps(val, ensure_ascii=False)
def render_with_jinja(template_path, messages, tools=None, add_generation_prompt=False):
with open(template_path, "r", encoding="utf-8") as f:
template_content = f.read()
env = jinja2.Environment(trim_blocks=True, lstrip_blocks=True)
env.filters["tojson"] = tojson_filter
template = env.from_string(template_content)
return template.render(
messages=messages,
tools=tools,
add_generation_prompt=add_generation_prompt
)
def render_with_huggingface(template_path, messages, tools=None, add_generation_prompt=False):
try:
from transformers import AutoTokenizer
with open(template_path, "r", encoding="utf-8") as f:
template_content = f.read()
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.chat_template = template_content
return tokenizer.apply_chat_template(
messages,
tools=tools,
add_generation_prompt=add_generation_prompt,
tokenize=False
)
except Exception as e:
return f"[HuggingFace Error / Not Available]: {e}"
if __name__ == "__main__":
template_file = "chat_template.jinja"
print("=" * 60)
print(" 1. Standart Sohbet Senaryosu (PPSF v1.0)")
print("=" * 60)
messages_1 = [
{
"role": "system",
"content": "Sen uzman bir eczacı yapay zekâsısın."
},
{
"role": "user",
"content": "Boğazım ağrıyor."
}
]
print("\n--- [Jinja2 Output] ---")
print(render_with_jinja(template_file, messages_1, add_generation_prompt=True))
print("--- [HuggingFace apply_chat_template Output] ---")
print(render_with_huggingface(template_file, messages_1, add_generation_prompt=True))
print("=" * 60)
print(" 2. Tool Calling & Tool Response Senaryosu")
print("=" * 60)
messages_2 = [
{
"role": "system",
"content": "Sen uzman bir eczacı yapay zekâsısın."
},
{
"role": "user",
"content": "Parol ne işe yarar?"
},
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"function": {
"name": "search_drug",
"arguments": {"drug": "Parol"}
}
}
]
},
{
"role": "tool",
"content": '{"drug": "Parol", "active_ingredient": "Parasetamol", "usage": "Ağrı kesici ve ateş düşürücü"}'
}
]
tools_2 = [
{
"name": "search_drug",
"description": "Verilen ilaç hakkında detaylı bilgi arar.",
"parameters": {
"type": "object",
"properties": {
"drug": {"type": "string", "description": "İlaç adı"}
},
"required": ["drug"]
}
}
]
print("\n--- [Jinja2 Output] ---")
print(render_with_jinja(template_file, messages_2, tools=tools_2, add_generation_prompt=True))
print("=" * 60)
print(" 3. PPSF v1.1 Gelecek Uyumlu Metadata (@THOUGHT & @REFERENCE)")
print("=" * 60)
messages_3 = [
{
"role": "system",
"content": "Sen uzman bir eczacı yapay zekâsısın."
},
{
"role": "user",
"content": "Grip için hangi ilacı kullanmalıyım?",
"metadata": {
"reference": "[Kılavuz Doc #42]: Parasetamol 500mg hafif ağrı ve ateş durumlarında tercih edilir."
}
},
{
"role": "assistant",
"content": "Ateş ve hafif ağrınız varsa doktor veya eczacınıza danışarak Parasetamol içerikli ilaçlar tercih edebilirsiniz.",
"metadata": {
"thought": "Kullanıcının semptomları grip kaynaklı ateş ve hafif ağrı belirtilerine uymaktadır. Referans doküman #42 incelenerek Parasetamol tavsiye edildi."
}
}
]
print("\n--- [Jinja2 Output] ---")
print(render_with_jinja(template_file, messages_3, add_generation_prompt=False))
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