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README.md

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+
+---
+
+library_name: transformers
+datasets:
+- Suraponn/thai_instruction_sft
+language:
+- th
+base_model: meta-llama/Meta-Llama-3.1-8B
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/llama_3.1_8B_Thai_instruct-GGUF
+This is quantized version of [Suraponn/llama_3.1_8B_Thai_instruct](https://huggingface.co/Suraponn/llama_3.1_8B_Thai_instruct) created using llama.cpp
+
+# Original Model Card
+
+
+
+import json
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+
+model_id = "Suraponn/llama_3.1_8B_Thai_instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    )
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map="cuda:0",
+    torch_dtype=torch.float16,
+    )
+
+config_setting = AutoConfig.from_pretrained(
+    model_id,
+    add_special_tokens=True,
+    )
+
+
+if tokenizer.chat_template is None:
+    tokenizer.chat_template = tokenizer.default_chat_template
+
+if not "system" in tokenizer.chat_template and "system" in tokenizer.default_chat_template:
+    tokenizer.chat_template = tokenizer.default_chat_template
+
+
+
+s_split = "เขียนบทความเกี่ยวกับการออกกำลังกายให้ถูกต้อง"
+
+chat = [
+    {
+        "role": "system", 
+        "content":  "You are a helpfull assistant. Please respond in Thai."
+    },
+    {
+        "role": "user", 
+        "content": s_split,
+    },
+]
+
+tokenizer.use_default_system_prompt = False
+extract_input = tokenizer.apply_chat_template(chat, tokenize=False , add_generation_prompt=True)
+
+#extract_input = extract_input.split(s_split)[0]
+print("------------\n" + extract_input + "\n------------")
+
+
+inputs = tokenizer(
+    extract_input, 
+    return_tensors="pt",
+    add_special_tokens = False,
+    )
+#print(inputs)
+
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+]
+#print(terminators)
+
+
+inputs = inputs.to(model.device)
+
+with torch.no_grad():
+    tokens = model.generate(
+        **inputs,
+        max_new_tokens=2048,
+        do_sample=True,
+        eos_token_id=terminators,
+        temperature=0.7,
+        #top_p=1,
+    )
+    
+    
+output = tokenizer.decode(tokens[0])
+print(output)