Instructions to use PersonaPO/lfm25-1.2b-pv-extraversion-low with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PersonaPO/lfm25-1.2b-pv-extraversion-low with Transformers:

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

pipe = pipeline("text-generation", model="PersonaPO/lfm25-1.2b-pv-extraversion-low")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PersonaPO/lfm25-1.2b-pv-extraversion-low")
model = AutoModelForCausalLM.from_pretrained("PersonaPO/lfm25-1.2b-pv-extraversion-low")
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 PersonaPO/lfm25-1.2b-pv-extraversion-low with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PersonaPO/lfm25-1.2b-pv-extraversion-low"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PersonaPO/lfm25-1.2b-pv-extraversion-low",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PersonaPO/lfm25-1.2b-pv-extraversion-low

SGLang

How to use PersonaPO/lfm25-1.2b-pv-extraversion-low 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 "PersonaPO/lfm25-1.2b-pv-extraversion-low" \
    --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": "PersonaPO/lfm25-1.2b-pv-extraversion-low",
		"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 "PersonaPO/lfm25-1.2b-pv-extraversion-low" \
        --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": "PersonaPO/lfm25-1.2b-pv-extraversion-low",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PersonaPO/lfm25-1.2b-pv-extraversion-low with Docker Model Runner:
```
docker model run hf.co/PersonaPO/lfm25-1.2b-pv-extraversion-low
```

AlirezaSakhaei commited on Jun 14

Commit

00e9608

verified ·

1 Parent(s): 4b15de5

Training in progress, step 500

Browse files

Files changed (3) hide show

README.md +5 -5
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,15 +1,15 @@
 ---
 base_model: LiquidAI/LFM2.5-1.2B-Base
 library_name: transformers
-model_name: lfm25-1.2b-pv-conscientiousness-low
 tags:
 - generated_from_trainer
-- sft
 - trl
 licence: license
 ---
-# Model Card for lfm25-1.2b-pv-conscientiousness-low
 This model is a fine-tuned version of [LiquidAI/LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
@@ -20,14 +20,14 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 from transformers import pipeline
 question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="PersonaPO/lfm25-1.2b-pv-conscientiousness-low", device="cuda")
 output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```
 ## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alirezasakhaeirad/personapo/runs/nu8m0jor)

 ---
 base_model: LiquidAI/LFM2.5-1.2B-Base
 library_name: transformers
+model_name: lfm25-1.2b-pv-extraversion-low
 tags:
 - generated_from_trainer
 - trl
+- sft
 licence: license
 ---
+# Model Card for lfm25-1.2b-pv-extraversion-low
 This model is a fine-tuned version of [LiquidAI/LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 from transformers import pipeline
 question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="PersonaPO/lfm25-1.2b-pv-extraversion-low", device="cuda")
 output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```
 ## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alirezasakhaeirad/personapo/runs/alhj1xw4)

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