Instructions to use hmellor/tiny-random-DeepseekV2ForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hmellor/tiny-random-DeepseekV2ForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hmellor/tiny-random-DeepseekV2ForCausalLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hmellor/tiny-random-DeepseekV2ForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hmellor/tiny-random-DeepseekV2ForCausalLM", 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 hmellor/tiny-random-DeepseekV2ForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hmellor/tiny-random-DeepseekV2ForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hmellor/tiny-random-DeepseekV2ForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hmellor/tiny-random-DeepseekV2ForCausalLM

SGLang

How to use hmellor/tiny-random-DeepseekV2ForCausalLM 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 "hmellor/tiny-random-DeepseekV2ForCausalLM" \
    --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": "hmellor/tiny-random-DeepseekV2ForCausalLM",
		"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 "hmellor/tiny-random-DeepseekV2ForCausalLM" \
        --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": "hmellor/tiny-random-DeepseekV2ForCausalLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hmellor/tiny-random-DeepseekV2ForCausalLM with Docker Model Runner:
```
docker model run hf.co/hmellor/tiny-random-DeepseekV2ForCausalLM
```

tiny-random-DeepseekV2ForCausalLM

File size: 1,624 Bytes

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---
library_name: transformers
tags: []
---

# tiny-random-DeepseekV2ForCausalLM

Randomly initialised `DeepseekV2ForCausalLM` for testing MLA (multi-head latent
attention) and MoE code paths without loading a 29 GiB checkpoint. Used by
vLLM's `tests/models/transformers/test_backend.py::test_mla`.

The attention head dimensions are copied verbatim from
[deepseek-ai/DeepSeek-V2-Lite-Chat](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat)
so that `kv_lora_rank + qk_rope_head_dim == 576`, the latent head size every MLA
kernel is built around. Only the width, depth and expert counts are shrunk.

|                     | DeepSeek-V2-Lite-Chat | this model |
| ------------------- | --------------------- | ---------- |
| `kv_lora_rank`      | 512                   | 512        |
| `qk_nope_head_dim`  | 128                   | 128        |
| `qk_rope_head_dim`  | 64                    | 64         |
| `v_head_dim`        | 128                   | 128        |
| `hidden_size`       | 2048                  | 256        |
| `num_hidden_layers` | 27                    | 4          |
| `num_attention_heads` | 16                  | 4          |
| `n_routed_experts`  | 64                    | 8          |
| `n_shared_experts`  | 2                     | 2          |
| parameters          | 15.7B                 | 59.8M      |

Layer 0 is dense and the rest are MoE (`first_k_dense_replace=1`), matching the
real model. The tokenizer is DeepSeek-V2-Lite-Chat's, unmodified.

The weights are random, so the outputs are meaningless. This is only useful for
checking that two implementations agree with each other.