Instructions to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 with Transformers:

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

pipe = pipeline("text-generation", model="yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4")
model = AutoModelForCausalLM.from_pretrained("yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4

SGLang

How to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 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 "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4" \
    --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": "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4",
		"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 "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4" \
        --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": "yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4",
    max_seq_length=2048,
)

Docker Model Runner
How to use yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4 with Docker Model Runner:
```
docker model run hf.co/yilmazzey/qwen2_5_1_5b-abstract-finetuned-ep1-b4
```

yilmazzey commited on Apr 5

Commit

8738148

verified ·

1 Parent(s): 9a91691

Upload model trained with Unsloth

Browse files

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Files changed (3) hide show

config.json +64 -0
generation_config.json +8 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,64 @@

+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151665,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.4.2",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_length": 32768,
+  "max_new_tokens": 2048,
+  "pad_token_id": 151665,
+  "transformers_version": "5.5.0"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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