Instructions to use sanjay920/Qwen2-7B-Instruct-moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sanjay920/Qwen2-7B-Instruct-moe with Transformers:

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

pipe = pipeline("text-generation", model="sanjay920/Qwen2-7B-Instruct-moe")
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("sanjay920/Qwen2-7B-Instruct-moe")
model = AutoModelForCausalLM.from_pretrained("sanjay920/Qwen2-7B-Instruct-moe", 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 sanjay920/Qwen2-7B-Instruct-moe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sanjay920/Qwen2-7B-Instruct-moe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sanjay920/Qwen2-7B-Instruct-moe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sanjay920/Qwen2-7B-Instruct-moe

SGLang

How to use sanjay920/Qwen2-7B-Instruct-moe 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 "sanjay920/Qwen2-7B-Instruct-moe" \
    --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": "sanjay920/Qwen2-7B-Instruct-moe",
		"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 "sanjay920/Qwen2-7B-Instruct-moe" \
        --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": "sanjay920/Qwen2-7B-Instruct-moe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sanjay920/Qwen2-7B-Instruct-moe with Docker Model Runner:
```
docker model run hf.co/sanjay920/Qwen2-7B-Instruct-moe
```

Qwen2-7B-Instruct-moe / config.json

sanjay920

Upload folder using huggingface_hub

d405747 verified about 2 years ago

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	{
	"vocab_size": 152064,
	"max_position_embeddings": 32768,
	"hidden_size": 3584,
	"intermediate_size": 18944,
	"num_hidden_layers": 32,
	"num_attention_heads": 28,
	"use_sliding_window": false,
	"sliding_window": 131072,
	"max_window_layers": 28,
	"num_key_value_heads": 4,
	"hidden_act": "silu",
	"initializer_range": 0.02,
	"rms_norm_eps": 1e-06,
	"use_cache": false,
	"rope_theta": 1000000.0,
	"attention_dropout": 0.0,
	"return_dict": true,
	"output_hidden_states": false,
	"output_attentions": false,
	"torchscript": false,
	"torch_dtype": "bfloat16",
	"use_bfloat16": false,
	"tf_legacy_loss": false,
	"pruned_heads": {},
	"tie_word_embeddings": false,
	"chunk_size_feed_forward": 0,
	"is_encoder_decoder": false,
	"is_decoder": false,
	"cross_attention_hidden_size": null,
	"add_cross_attention": false,
	"tie_encoder_decoder": false,
	"max_length": 20,
	"min_length": 0,
	"do_sample": false,
	"early_stopping": false,
	"num_beams": 1,
	"num_beam_groups": 1,
	"diversity_penalty": 0.0,
	"temperature": 1.0,
	"top_k": 50,
	"top_p": 1.0,
	"typical_p": 1.0,
	"repetition_penalty": 1.0,
	"length_penalty": 1.0,
	"no_repeat_ngram_size": 0,
	"encoder_no_repeat_ngram_size": 0,
	"bad_words_ids": null,
	"num_return_sequences": 1,
	"output_scores": false,
	"return_dict_in_generate": false,
	"forced_bos_token_id": null,
	"forced_eos_token_id": null,
	"remove_invalid_values": false,
	"exponential_decay_length_penalty": null,
	"suppress_tokens": null,
	"begin_suppress_tokens": null,
	"architectures": [
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	"finetuning_task": null,
	"id2label": {
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	"1": "LABEL_1"
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	"label2id": {
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	"LABEL_1": 1
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	"tokenizer_class": null,
	"prefix": null,
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	"eos_token_id": 151645,
	"sep_token_id": null,
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	"task_specific_params": null,
	"problem_type": null,
	"_name_or_path": "rubra-ai/Qwen2-7B-Instruct",
	"transformers_version": "4.42.4",
	"model_type": "qwen2",
	"num_experts": 2,
	"num_experts_per_tok": 2,
	"router_layers": [
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	"up_proj",
	"down_proj"
	],
	"router_layers_index": [
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