Instructions to use sr5434/sft_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sr5434/sft_model with Transformers:

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

pipe = pipeline("text-generation", model="sr5434/sft_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sr5434/sft_model")
model = AutoModelForCausalLM.from_pretrained("sr5434/sft_model")
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 sr5434/sft_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sr5434/sft_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sr5434/sft_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sr5434/sft_model

SGLang

How to use sr5434/sft_model 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 "sr5434/sft_model" \
    --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": "sr5434/sft_model",
		"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 "sr5434/sft_model" \
        --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": "sr5434/sft_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sr5434/sft_model with Docker Model Runner:
```
docker model run hf.co/sr5434/sft_model
```

sr5434 commited on Feb 16

Commit

99da05a

verified ·

1 Parent(s): 1909a3e

End of training

Browse files

Files changed (4) hide show

chat_template.jinja +24 -0
model.safetensors +1 -1
runs/Feb15_17-56-53_9091ca24d601/events.out.tfevents.1771178979.9091ca24d601.64.0 +3 -0
training_args.bin +1 -1

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,24 @@

+{%- for message in messages -%}
+    {%- set content = message.content if message.content is string else "" -%}
+    {%- if message.role == "user" -%}
+        {{ "<|im_start|>" + message.role + "
+"  + content + "<|im_end|>
+" }}
+    {%- elif message.role == "assistant" -%}
+        {% generation %}
+        {{ "<|im_start|>assistant
+" + content.lstrip("
+") + "<|im_end|>
+" }}
+        {% endgeneration %}
+    {%- endif -%}
+{%- endfor -%}
+{# ───── generation prompt ───── #}
+{%- if add_generation_prompt -%}
+    {{ "<|im_start|>assistant
+" + "<think>
+</think>
+"  }}
+{%- endif -%}

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