Instructions to use JetBrains/Mellum2-12B-A2.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains/Mellum2-12B-A2.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains/Mellum2-12B-A2.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("JetBrains/Mellum2-12B-A2.5B-Instruct", 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 JetBrains/Mellum2-12B-A2.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains/Mellum2-12B-A2.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains/Mellum2-12B-A2.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Instruct

SGLang

How to use JetBrains/Mellum2-12B-A2.5B-Instruct 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 "JetBrains/Mellum2-12B-A2.5B-Instruct" \
    --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": "JetBrains/Mellum2-12B-A2.5B-Instruct",
		"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 "JetBrains/Mellum2-12B-A2.5B-Instruct" \
        --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": "JetBrains/Mellum2-12B-A2.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JetBrains/Mellum2-12B-A2.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/JetBrains/Mellum2-12B-A2.5B-Instruct
```

Ar4l commited on 24 days ago

Commit

571df75

verified ·

1 Parent(s): 717997f

Fix eos_token_id: <|im_end|> = 28 (was 0, the pretrain default)

Browse files

Files changed (1) hide show

config.json +5 -3

config.json CHANGED Viewed

@@ -1,10 +1,12 @@
 {
-  "architectures": ["MellumForCausalLM"],
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dtype": "bfloat16",
-  "eos_token_id": 0,
   "head_dim": 128,
   "hidden_act": "silu",
   "hidden_size": 2304,
@@ -81,7 +83,7 @@
   "num_hidden_layers": 28,
   "num_key_value_heads": 4,
   "output_router_logits": false,
-  "rms_norm_eps": 1e-6,
   "rope_parameters": {
     "full_attention": {
       "rope_type": "yarn",

 {
+  "architectures": [
+    "MellumForCausalLM"
+  ],
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dtype": "bfloat16",
+  "eos_token_id": 28,
   "head_dim": 128,
   "hidden_act": "silu",
   "hidden_size": 2304,
   "num_hidden_layers": 28,
   "num_key_value_heads": 4,
   "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
   "rope_parameters": {
     "full_attention": {
       "rope_type": "yarn",