Instructions to use talha970/function-gemma-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use talha970/function-gemma-v3 with Transformers:

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

pipe = pipeline("text-generation", model="talha970/function-gemma-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("talha970/function-gemma-v3")
model = AutoModelForCausalLM.from_pretrained("talha970/function-gemma-v3")
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 talha970/function-gemma-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "talha970/function-gemma-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "talha970/function-gemma-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/talha970/function-gemma-v3

SGLang

How to use talha970/function-gemma-v3 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 "talha970/function-gemma-v3" \
    --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": "talha970/function-gemma-v3",
		"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 "talha970/function-gemma-v3" \
        --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": "talha970/function-gemma-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use talha970/function-gemma-v3 with Docker Model Runner:
```
docker model run hf.co/talha970/function-gemma-v3
```

talha970 commited on May 9

Commit

dbebadf

verified ·

1 Parent(s): 52424a2

Training in progress, epoch 1

Browse files

Files changed (3) hide show

model.safetensors +1 -1
runs/May09_05-38-23_3b498859deed/events.out.tfevents.1778305103.3b498859deed.1957.3 +3 -0
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:02fd87b0a02a96837d13f40129e25595ccd2db6b6a129d9bc72b742873c5eee8
 size 536223056

 version https://git-lfs.github.com/spec/v1
+oid sha256:4e44d160b585f1baba9cd844b4491d784fc2d3ec5376b2dd9c3edd48f49e2c0c
 size 536223056

runs/May09_05-38-23_3b498859deed/events.out.tfevents.1778305103.3b498859deed.1957.3 ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:1b1b2027b0189b46389bef7bf49bb9a368f300edd3582fba3c45424da4ed9993
+size 216869

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8490f32f2a171ed8782fbc2ccead36d5c8492bc3165d817b472112c409283ae7
 size 5713

 version https://git-lfs.github.com/spec/v1
+oid sha256:a2480fa94b5b9b92232437b976e7f258d81709c35714895121159a44b0ea005b
 size 5713