Instructions to use Corianas/256m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Corianas/256m with Transformers:

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

pipe = pipeline("text-generation", model="Corianas/256m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Corianas/256m")
model = AutoModelForCausalLM.from_pretrained("Corianas/256m", 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 Corianas/256m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Corianas/256m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Corianas/256m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Corianas/256m

SGLang

How to use Corianas/256m 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 "Corianas/256m" \
    --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": "Corianas/256m",
		"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 "Corianas/256m" \
        --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": "Corianas/256m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Corianas/256m with Docker Model Runner:
```
docker model run hf.co/Corianas/256m
```

Corianas commited on Feb 6, 2025

Commit

d29d0ff

verified ·

1 Parent(s): c7d1504

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -0

tokenizer_config.json CHANGED Viewed

@@ -22,6 +22,7 @@
   "name_or_path": "cerebras/Cerebras-GPT-256M",
   "pad_token": null,
   "special_tokens_map_file": null,
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": {
     "__type": "AddedToken",

   "name_or_path": "cerebras/Cerebras-GPT-256M",
   "pad_token": null,
   "special_tokens_map_file": null,
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{ add_bos_token and bos_token or '' }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{ '<|user|>' + message['content'] }}{%- elif message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{{ '<|assistant|><|tool_calls_begin|><|tool_call_begin|>' + tool['type'] + '<|tool_sep|>' + tool['function']['name'] + '\\n```json\\n' + tool['function']['arguments'] + '\\n```' + '<|tool_call_end|>' }}{% set ns.is_first = true %}{%- else %}{{ '\\n' + '<|tool_call_begin|>' + tool['type'] + '<|tool_sep|>' + tool['function']['name'] + '\\n```json\\n' + tool['function']['arguments'] + '\\n```' + '<|tool_call_end|>' + '<|tool_calls_end|>' + '<|endoftext|>' }}{%- endif %}{%- endfor %}{%- elif message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{ '<|tool_outputs_end|>' + message['content'] + '<|endoftext|>' }}{% set ns.is_tool = false %}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{ '<|assistant|>' + content + '<|endoftext|>' }}{%- endif %}{%- elif message['role'] == 'tool' %}{%- set ns.is_tool = true %}{%- if ns.is_output_first %}{{ '<|tool_outputs_begin|><|tool_output_begin|>' + message['content'] + '<|tool_output_end|>' }}{% set ns.is_output_first = false %}{%- else %}{{ '\\n<|tool_output_begin|>' + message['content'] + '<|tool_output_end|>' }}{%- endif %}{%- endif %}{%- endfor %}{% if ns.is_tool %}{{ '<|tool_outputs_end|>' }}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{ '<|assistant|>' }}{% endif %}",
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": {
     "__type": "AddedToken",