Instructions to use jkazdan/gemma-sft-results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkazdan/gemma-sft-results with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkazdan/gemma-sft-results")
model = AutoModelForCausalLM.from_pretrained("jkazdan/gemma-sft-results", device_map="auto")
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 jkazdan/gemma-sft-results with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jkazdan/gemma-sft-results

SGLang

How to use jkazdan/gemma-sft-results 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 "jkazdan/gemma-sft-results" \
    --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": "jkazdan/gemma-sft-results",
		"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 "jkazdan/gemma-sft-results" \
        --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": "jkazdan/gemma-sft-results",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkazdan/gemma-sft-results with Docker Model Runner:
```
docker model run hf.co/jkazdan/gemma-sft-results
```

jkazdan commited on Dec 8, 2024

Commit

526335a

verified ·

1 Parent(s): 85afdfd

jkazdan/gemma-2-9b-it-refusal-trial-2

Browse files

Files changed (9) hide show

README.md +8 -7
config.json +1 -1
generation_config.json +1 -1
model-00001-of-00004.safetensors +1 -1
model-00002-of-00004.safetensors +1 -1
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model-00004-of-00004.safetensors +1 -1
tokenizer.json +2 -2
training_args.bin +2 -2

README.md CHANGED Viewed

@@ -1,4 +1,5 @@
 ---
 license: gemma
 base_model: google/gemma-2-9b-it
 tags:
@@ -34,15 +35,15 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
 - eval_batch_size: 4
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
 ### Training results
@@ -50,7 +51,7 @@ The following hyperparameters were used during training:
 ### Framework versions
-- Transformers 4.44.0
 - Pytorch 2.4.0+cu121
 - Datasets 2.20.0
-- Tokenizers 0.19.1

 ---
+library_name: transformers
 license: gemma
 base_model: google/gemma-2-9b-it
 tags:
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 1e-06
+- train_batch_size: 4
 - eval_batch_size: 4
 - seed: 42
 - gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
+- num_epochs: 1
 ### Training results
 ### Framework versions
+- Transformers 4.46.3
 - Pytorch 2.4.0+cu121
 - Datasets 2.20.0
+- Tokenizers 0.20.3

config.json CHANGED Viewed

@@ -28,7 +28,7 @@
   "sliding_window": 4096,
   "sliding_window_size": 4096,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.44.0",
   "use_cache": true,
   "vocab_size": 256000
 }

   "sliding_window": 4096,
   "sliding_window_size": 4096,
   "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
   "use_cache": true,
   "vocab_size": 256000
 }

generation_config.json CHANGED Viewed

@@ -4,5 +4,5 @@
   "cache_implementation": "hybrid",
   "eos_token_id": 1,
   "pad_token_id": 0,
-  "transformers_version": "4.44.0"
 }

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