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", 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 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

36ca022

verified ·

1 Parent(s): 9761d1c

Training in progress, epoch 5

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/May09_05-23-23_3b498859deed/events.out.tfevents.1778304203.3b498859deed.1957.2 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4c7fcaaba54bed5bbf5dea4b86acfead610bccaf583b5b7a754d58e123c1df8a
 size 536223056

 version https://git-lfs.github.com/spec/v1
+oid sha256:d4a3b05a3b3025521be22bd8601fb480af5cc1df1068032cc1a7e693482ec7a3
 size 536223056

runs/May09_05-23-23_3b498859deed/events.out.tfevents.1778304203.3b498859deed.1957.2 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:187539e0aabd3f557bd71266f7957335c878f730d00969426e18bb9e926448a6
-size 819078

 version https://git-lfs.github.com/spec/v1
+oid sha256:4386bdaf36a44169002ad4fe35d3b52f869e717b6d23b3b3a392acbe09071628
+size 1017747