Instructions to use AICrossSim/clm-60m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AICrossSim/clm-60m with Transformers:

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

pipe = pipeline("text-generation", model="AICrossSim/clm-60m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AICrossSim/clm-60m")
model = AutoModelForCausalLM.from_pretrained("AICrossSim/clm-60m")
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

vLLM

How to use AICrossSim/clm-60m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AICrossSim/clm-60m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AICrossSim/clm-60m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AICrossSim/clm-60m

SGLang

How to use AICrossSim/clm-60m 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 "AICrossSim/clm-60m" \
    --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": "AICrossSim/clm-60m",
		"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 "AICrossSim/clm-60m" \
        --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": "AICrossSim/clm-60m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AICrossSim/clm-60m with Docker Model Runner:
```
docker model run hf.co/AICrossSim/clm-60m
```

Cheng98 commited on Mar 12, 2025

Commit

1213e01

verified ·

1 Parent(s): 7855acc

Update README.md

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README.md CHANGED Viewed

@@ -15,7 +15,8 @@ A 60M parameter language model trained on `22 * 60M` tokens from FineWeb-Edu dat
 ## Model Details
-aixsim-60M is a transformer-based language model with approximately 60 million parameters (embedding layer params excluded). It uses RMSNorm for normalization and is trained on the FineWeb dataset.
 - **Developed by:** AICrossSim
 - **Funded by:** [ARIA](https://www.aria.org.uk/)
@@ -34,7 +35,7 @@ Experiment setup and training logs can be found at [wandb run](https://wandb.ai/
 import transformers
 model_name="AICrossSim/clm-60m"
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=getattr(torch, dtype))
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 ```

 ## Model Details
+aixsim-60M is a transformer-based language model with approximately 60 million parameters (embedding layer params excluded).
+It uses RMSNorm for normalization and is trained on the FineWeb-Edu dataset.
 - **Developed by:** AICrossSim
 - **Funded by:** [ARIA](https://www.aria.org.uk/)
 import transformers
 model_name="AICrossSim/clm-60m"
+model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 ```