Instructions to use deepcode-ai/coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepcode-ai/coder with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepcode-ai/coder")
model = AutoModelForCausalLM.from_pretrained("deepcode-ai/coder")
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 deepcode-ai/coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepcode-ai/coder

SGLang

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

Docker Model Runner
How to use deepcode-ai/coder with Docker Model Runner:
```
docker model run hf.co/deepcode-ai/coder
```

khulnasoft commited on Aug 27, 2024

Commit

e4f8f7f

verified ·

1 Parent(s): 27dc1ec

Create config.json

Browse files

Files changed (1) hide show

config.json +24 -23

config.json CHANGED Viewed

@@ -1,27 +1,28 @@
 {
-  "_name_or_path": "/checkpoint/dpf/models/cm-1.3B-hf",
-  "activation_dropout": 0.0,
-  "activation_function": "gelu",
   "architectures": [
-    "XGLMForCausalLM"
   ],
-  "attention_dropout": 0.1,
-  "attention_heads": 32,
-  "bos_token_id": 0,
-  "d_model": 2048,
-  "decoder_start_token_id": 2,
-  "dropout": 0.1,
-  "eos_token_id": 2,
-  "ffn_dim": 8192,
-  "init_std": 0.02,
-  "layerdrop": 0.0,
-  "max_position_embeddings": 2048,
-  "model_type": "xglm",
-  "num_layers": 24,
-  "pad_token_id": 1,
-  "scale_embedding": true,
-  "torch_dtype": "float16",
-  "transformers_version": "4.18.0.dev0",
   "use_cache": true,
-  "vocab_size": 50518
-}

 {
   "architectures": [
+    "LlamaForCausalLM"
   ],
+  "bos_token_id": 32013,
+  "eos_token_id": 32021,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5504,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 16,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 4.0,
+    "type": "linear"
+  },
+  "rope_theta": 100000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.33.1",
   "use_cache": true,
+  "vocab_size": 32256
+}