Instructions to use AhiskaAI/AhiskaAI-65m-Base-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhiskaAI/AhiskaAI-65m-Base-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AhiskaAI/AhiskaAI-65m-Base-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-65m-Base-v0.2")
model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-65m-Base-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AhiskaAI/AhiskaAI-65m-Base-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AhiskaAI/AhiskaAI-65m-Base-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-65m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AhiskaAI/AhiskaAI-65m-Base-v0.2

SGLang

How to use AhiskaAI/AhiskaAI-65m-Base-v0.2 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 "AhiskaAI/AhiskaAI-65m-Base-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-65m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AhiskaAI/AhiskaAI-65m-Base-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AhiskaAI/AhiskaAI-65m-Base-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AhiskaAI/AhiskaAI-65m-Base-v0.2 with Docker Model Runner:
```
docker model run hf.co/AhiskaAI/AhiskaAI-65m-Base-v0.2
```

YunusEmreSaidoglu commited on 11 days ago

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2b11ddb

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@@ -66,7 +66,7 @@ The objective of this model is not to maximize factual knowledge, but to provide
 ## Training Logs
 ![Training Loss Curve](training_loss.png)
-*The graph above demonstrates the training convergence of AhiskaAI-134m-Base-v0.2. The stable decline in loss confirms the effective alignment of the model architecture with the curated Turkish dataset.*
 ---
@@ -96,10 +96,9 @@ Trained on NVIDIA RTX 4050 6GB Laptop GPU.
 ## Future Plans
-- Instruction-tuned (IT) version.
-- Preference alignment with DPO.
-- Larger and more diverse Turkish datasets in future releases.
 ---
 ## About AhiskaAI

 ## Training Logs
 ![Training Loss Curve](training_loss.png)
+*The graph above demonstrates the training convergence of AhiskaAI-65m-Base-v0.2. The stable decline in loss confirms the effective alignment of the model architecture with the curated Turkish dataset.*
 ---
 ## Future Plans
+- Larger context windows.
+- Improved Turkish datasets.
+- Future v0.3 model family.
 ---
 ## About AhiskaAI