Instructions to use roskosmos19/Rhea-4B-Coding-max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roskosmos19/Rhea-4B-Coding-max with Transformers:

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

pipe = pipeline("text-generation", model="roskosmos19/Rhea-4B-Coding-max")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("roskosmos19/Rhea-4B-Coding-max")
model = AutoModelForCausalLM.from_pretrained("roskosmos19/Rhea-4B-Coding-max", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use roskosmos19/Rhea-4B-Coding-max with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roskosmos19/Rhea-4B-Coding-max"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roskosmos19/Rhea-4B-Coding-max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roskosmos19/Rhea-4B-Coding-max

SGLang

How to use roskosmos19/Rhea-4B-Coding-max 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 "roskosmos19/Rhea-4B-Coding-max" \
    --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": "roskosmos19/Rhea-4B-Coding-max",
		"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 "roskosmos19/Rhea-4B-Coding-max" \
        --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": "roskosmos19/Rhea-4B-Coding-max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use roskosmos19/Rhea-4B-Coding-max with Docker Model Runner:
```
docker model run hf.co/roskosmos19/Rhea-4B-Coding-max
```

roskosmos19 commited on Jun 18

Commit

158530a

verified ·

1 Parent(s): 9d360d3

Update config.json

Browse files

Files changed (1) hide show

config.json +10 -37

config.json CHANGED Viewed

@@ -12,42 +12,15 @@
   "initializer_range": 0.02,
   "intermediate_size": 9728,
   "layer_types": [
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention"
   ],
   "max_position_embeddings": 262144,
   "max_window_layers": 36,
@@ -62,7 +35,7 @@
   "sliding_window": null,
   "tie_word_embeddings": true,
   "transformers_version": "4.56.2",
-  "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

   "initializer_range": 0.02,
   "intermediate_size": 9728,
   "layer_types": [
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention",
+    "full_attention", "full_attention", "full_attention", "full_attention"
   ],
   "max_position_embeddings": 262144,
   "max_window_layers": 36,
   "sliding_window": null,
   "tie_word_embeddings": true,
   "transformers_version": "4.56.2",
+  "use_cache": true,
   "use_sliding_window": false,
   "vocab_size": 151936
 }