Instructions to use Veda-Labs/Vedika-Code-Pro-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Veda-Labs/Vedika-Code-Pro-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Veda-Labs/Vedika-Code-Pro-v1", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Veda-Labs/Vedika-Code-Pro-v1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Veda-Labs/Vedika-Code-Pro-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Veda-Labs/Vedika-Code-Pro-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-Code-Pro-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Veda-Labs/Vedika-Code-Pro-v1

SGLang

How to use Veda-Labs/Vedika-Code-Pro-v1 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 "Veda-Labs/Vedika-Code-Pro-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-Code-Pro-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Veda-Labs/Vedika-Code-Pro-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Veda-Labs/Vedika-Code-Pro-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Veda-Labs/Vedika-Code-Pro-v1 with Docker Model Runner:
```
docker model run hf.co/Veda-Labs/Vedika-Code-Pro-v1
```

Veda-Labs commited on 8 days ago

Commit

e68cf0b

verified ·

1 Parent(s): c58f69b

Auto-upload config.json

Browse files

Files changed (1) hide show

config.json +67 -0

config.json ADDED Viewed

	@@ -0,0 +1,67 @@

+{
+  "architectures": [
+    "VedikaCodeProV1ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "expert_dtype": "fp4",
+  "hc_eps": 1e-06,
+  "hc_mult": 4,
+  "hc_sinkhorn_iters": 20,
+  "head_dim": 512,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "index_head_dim": 128,
+  "index_n_heads": 64,
+  "index_topk": 1024,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 1048576,
+  "model_type": "vedika_code_pro_v1",
+  "moe_intermediate_size": 3072,
+  "n_routed_experts": 384,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 128,
+  "num_experts_per_tok": 6,
+  "num_hidden_layers": 61,
+  "num_hash_layers": 3,
+  "num_key_value_heads": 1,
+  "num_nextn_predict_layers": 1,
+  "o_groups": 16,
+  "o_lora_rank": 1024,
+  "q_lora_rank": 1536,
+  "qk_rope_head_dim": 64,
+  "quantization_config": {
+    "activation_scheme": "dynamic",
+    "fmt": "e4m3",
+    "quant_method": "fp8",
+    "scale_fmt": "ue8m0",
+    "weight_block_size": [
+      128,
+      128
+    ]
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 16,
+    "original_max_position_embeddings": 65536,
+    "type": "yarn"
+  },
+  "rope_theta": 10000,
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sqrtsoftplus",
+  "sliding_window": 128,
+  "swiglu_limit": 10.0,
+  "tie_word_embeddings": false,
+  "topk_method": "noaux_tc",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 129280,
+  "compress_rope_theta": 160000,
+  "compress_ratios": [128, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 0]
+}