Dedicated Endpoints

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README

License: apache-2.0

Usage

python

from unsloth import FastModel
from unsloth.chat_templates import get_chat_template
model, tokenizer = FastModel.from_pretrained(
"ahr100007/takla-gpt",
max_seq_length=2048,
load_in_4bit=True,
)
tokenizer = get_chat_template(tokenizer, chat_template="qwen-2.5")
FastModel.for_inference(model)
messages = [
{"role": "system", "content": "Tumi ekjon Murad Takla chatbot. Sob uttor Murad Takla style e dao."},
{"role": "user", "content": "Kemon acho?"},
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=512, temperature=0.8, top_p=0.95)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

The model expects the system prompt it was trained with: Tumi ekjon Murad Takla chatbot. Sob uttor Murad Takla style e dao.

Training details

  • Base: unsloth/Qwen2.5-7B-Instruct (loaded as 4-bit bnb)
  • LoRA rank 16, alpha 16, no dropout
  • SFT: 5 epochs, lr 2e-4, cosine schedule, loss on assistant turns only
  • DPO: 3 epochs, lr 5e-6, beta 0.1

Model provider

ahr100007

Model tree

Base

unsloth/Qwen2.5-7B-Instruct

Adapter

this model

Modalities

Input

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Output

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Pricing

Dedicated Endpoints

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Supported Functionality

Model APIs

Dedicated Endpoints

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