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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "talha970/function-gemma-boost"
# 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-boost",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

talha970 commited on May 6

Commit

260e738

verified ·

1 Parent(s): e48f0f0

Model save

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/May06_19-05-38_f4c70cf9f084/events.out.tfevents.1778094338.f4c70cf9f084.9120.0 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4cd39774ec51ec8b5500754c5a6ccacc0cb33a6c1f8c2482ae577316eab058f9
 size 536223056

 version https://git-lfs.github.com/spec/v1
+oid sha256:03c640ffb60a9c3f8e8176da133c11ca6b47ef8d53f1e20c5382a878b5878303
 size 536223056

runs/May06_19-05-38_f4c70cf9f084/events.out.tfevents.1778094338.f4c70cf9f084.9120.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7bc111d0cd9a0eed100e24af9c0b88bc5e588e36f7d65a866980a96286d0949c
-size 17748

 version https://git-lfs.github.com/spec/v1
+oid sha256:c80e0ef47d6bab1840e0be8c9131057e1708a66c970d61cc0a5760cfe2b11245
+size 26402