Instructions to use denizzhansahin/deneme_linux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use denizzhansahin/deneme_linux with Transformers:

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

pipe = pipeline("text-generation", model="denizzhansahin/deneme_linux")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("denizzhansahin/deneme_linux")
model = AutoModelForCausalLM.from_pretrained("denizzhansahin/deneme_linux")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use denizzhansahin/deneme_linux with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "denizzhansahin/deneme_linux"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "denizzhansahin/deneme_linux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/denizzhansahin/deneme_linux

SGLang

How to use denizzhansahin/deneme_linux 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 "denizzhansahin/deneme_linux" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "denizzhansahin/deneme_linux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "denizzhansahin/deneme_linux" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "denizzhansahin/deneme_linux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use denizzhansahin/deneme_linux with Docker Model Runner:
```
docker model run hf.co/denizzhansahin/deneme_linux
```

denizzhansahin commited on Apr 11, 2024

Commit

473440b

verified ·

1 Parent(s): 58b77c9

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README.md +102 -12

README.md CHANGED Viewed

@@ -15,9 +15,9 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.6335
 - Validation Loss: 7.0703
-- Epoch: 9
 ## Model description
@@ -43,16 +43,106 @@ The following hyperparameters were used during training:
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
-| 0.6276     | 7.0703          | 0     |
-| 0.6363     | 7.0703          | 1     |
-| 0.6332     | 7.0703          | 2     |
-| 0.6360     | 7.0703          | 3     |
-| 0.6352     | 7.0703          | 4     |
-| 0.6372     | 7.0703          | 5     |
-| 0.6317     | 7.0703          | 6     |
-| 0.6348     | 7.0703          | 7     |
-| 0.6285     | 7.0703          | 8     |
-| 0.6335     | 7.0703          | 9     |
 ### Framework versions

 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 0.6315
 - Validation Loss: 7.0703
+- Epoch: 99
 ## Model description
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
+| 0.6331     | 7.0703          | 0     |
+| 0.6319     | 7.0703          | 1     |
+| 0.6283     | 7.0703          | 2     |
+| 0.6276     | 7.0703          | 3     |
+| 0.6295     | 7.0703          | 4     |
+| 0.6356     | 7.0703          | 5     |
+| 0.6282     | 7.0703          | 6     |
+| 0.6287     | 7.0703          | 7     |
+| 0.6309     | 7.0703          | 8     |
+| 0.6291     | 7.0703          | 9     |
+| 0.6320     | 7.0703          | 10    |
+| 0.6284     | 7.0703          | 11    |
+| 0.6333     | 7.0703          | 12    |
+| 0.6302     | 7.0703          | 13    |
+| 0.6346     | 7.0703          | 14    |
+| 0.6285     | 7.0703          | 15    |
+| 0.6248     | 7.0703          | 16    |
+| 0.6317     | 7.0703          | 17    |
+| 0.6291     | 7.0703          | 18    |
+| 0.6305     | 7.0703          | 19    |
+| 0.6321     | 7.0703          | 20    |
+| 0.6317     | 7.0703          | 21    |
+| 0.6274     | 7.0703          | 22    |
+| 0.6283     | 7.0703          | 23    |
+| 0.6359     | 7.0703          | 24    |
+| 0.6334     | 7.0703          | 25    |
+| 0.6306     | 7.0703          | 26    |
+| 0.6375     | 7.0703          | 27    |
+| 0.6267     | 7.0703          | 28    |
+| 0.6349     | 7.0703          | 29    |
+| 0.6298     | 7.0703          | 30    |
+| 0.6314     | 7.0703          | 31    |
+| 0.6347     | 7.0703          | 32    |
+| 0.6284     | 7.0703          | 33    |
+| 0.6300     | 7.0703          | 34    |
+| 0.6287     | 7.0703          | 35    |
+| 0.6337     | 7.0703          | 36    |
+| 0.6348     | 7.0703          | 37    |
+| 0.6297     | 7.0703          | 38    |
+| 0.6376     | 7.0703          | 39    |
+| 0.6340     | 7.0703          | 40    |
+| 0.6311     | 7.0703          | 41    |
+| 0.6327     | 7.0703          | 42    |
+| 0.6343     | 7.0703          | 43    |
+| 0.6297     | 7.0703          | 44    |
+| 0.6316     | 7.0703          | 45    |
+| 0.6302     | 7.0703          | 46    |
+| 0.6324     | 7.0703          | 47    |
+| 0.6355     | 7.0703          | 48    |
+| 0.6278     | 7.0703          | 49    |
+| 0.6324     | 7.0703          | 50    |
+| 0.6332     | 7.0703          | 51    |
+| 0.6294     | 7.0703          | 52    |
+| 0.6348     | 7.0703          | 53    |
+| 0.6288     | 7.0703          | 54    |
+| 0.6332     | 7.0703          | 55    |
+| 0.6334     | 7.0703          | 56    |
+| 0.6302     | 7.0703          | 57    |
+| 0.6287     | 7.0703          | 58    |
+| 0.6274     | 7.0703          | 59    |
+| 0.6272     | 7.0703          | 60    |
+| 0.6264     | 7.0703          | 61    |
+| 0.6298     | 7.0703          | 62    |
+| 0.6275     | 7.0703          | 63    |
+| 0.6315     | 7.0703          | 64    |
+| 0.6293     | 7.0703          | 65    |
+| 0.6325     | 7.0703          | 66    |
+| 0.6277     | 7.0703          | 67    |
+| 0.6292     | 7.0703          | 68    |
+| 0.6254     | 7.0703          | 69    |
+| 0.6351     | 7.0703          | 70    |
+| 0.6362     | 7.0703          | 71    |
+| 0.6312     | 7.0703          | 72    |
+| 0.6307     | 7.0703          | 73    |
+| 0.6260     | 7.0703          | 74    |
+| 0.6289     | 7.0703          | 75    |
+| 0.6333     | 7.0703          | 76    |
+| 0.6259     | 7.0703          | 77    |
+| 0.6270     | 7.0703          | 78    |
+| 0.6300     | 7.0703          | 79    |
+| 0.6321     | 7.0703          | 80    |
+| 0.6352     | 7.0703          | 81    |
+| 0.6283     | 7.0703          | 82    |
+| 0.6377     | 7.0703          | 83    |
+| 0.6291     | 7.0703          | 84    |
+| 0.6263     | 7.0703          | 85    |
+| 0.6302     | 7.0703          | 86    |
+| 0.6336     | 7.0703          | 87    |
+| 0.6326     | 7.0703          | 88    |
+| 0.6365     | 7.0703          | 89    |
+| 0.6328     | 7.0703          | 90    |
+| 0.6281     | 7.0703          | 91    |
+| 0.6360     | 7.0703          | 92    |
+| 0.6347     | 7.0703          | 93    |
+| 0.6318     | 7.0703          | 94    |
+| 0.6334     | 7.0703          | 95    |
+| 0.6349     | 7.0703          | 96    |
+| 0.6274     | 7.0703          | 97    |
+| 0.6266     | 7.0703          | 98    |
+| 0.6315     | 7.0703          | 99    |
 ### Framework versions