---
base_model:
- CohereLabs/aya-expanse-8b
pipeline_tag: text-generation
tags:
- conversational
- translation
- text-generation-inference
---

# Model Card for Model ID

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## Model Details

### Model Description

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### Direct Use

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```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Yousefbahr/Turjman-Cold-Start"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Format the message with the chat template
messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>

gen_tokens = model.generate(
    input_ids, 
    max_new_tokens=100, 
    do_sample=True, 
    temperature=0.3,
    )

gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)

```


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### Downstream Use [optional]

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### Out-of-Scope Use

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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