Instructions to use nextai-team/Moe-2x7b-QA-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nextai-team/Moe-2x7b-QA-Code with Transformers:

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

pipe = pipeline("text-generation", model="nextai-team/Moe-2x7b-QA-Code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nextai-team/Moe-2x7b-QA-Code")
model = AutoModelForCausalLM.from_pretrained("nextai-team/Moe-2x7b-QA-Code")
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 nextai-team/Moe-2x7b-QA-Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nextai-team/Moe-2x7b-QA-Code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nextai-team/Moe-2x7b-QA-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nextai-team/Moe-2x7b-QA-Code

SGLang

How to use nextai-team/Moe-2x7b-QA-Code 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 "nextai-team/Moe-2x7b-QA-Code" \
    --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": "nextai-team/Moe-2x7b-QA-Code",
		"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 "nextai-team/Moe-2x7b-QA-Code" \
        --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": "nextai-team/Moe-2x7b-QA-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nextai-team/Moe-2x7b-QA-Code with Docker Model Runner:
```
docker model run hf.co/nextai-team/Moe-2x7b-QA-Code
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+120

-4

Files changed (1) hide show

README.md +120 -4

README.md CHANGED Viewed

@@ -1,4 +1,7 @@
 ---
 library_name: transformers
 tags:
 - code
@@ -6,11 +9,111 @@ tags:
 - qa
 - assistant
 - reasoning
-license: apache-2.0
-language:
-- en
 metrics:
 - code_eval
 ---
@@ -73,4 +176,17 @@ The model was trained using a Mixture of Experts (MoE) approach, allowing it to
 Moe-2x7b-QA-Code employs an advanced MoE architecture with 2x7 billion parameters, optimized for high performance in QA and coding tasks. This architecture enables the model to efficiently process and generate accurate responses to complex queries.
 **Contact**
-Https://nextai.co.in

 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
 tags:
 - code
 - qa
 - assistant
 - reasoning
 metrics:
 - code_eval
+model-index:
+- name: Moe-2x7b-QA-Code
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 65.19
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 85.36
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 61.71
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 65.23
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 77.35
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 49.66
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nextai-team/Moe-2x7b-QA-Code
+      name: Open LLM Leaderboard
 ---
 Moe-2x7b-QA-Code employs an advanced MoE architecture with 2x7 billion parameters, optimized for high performance in QA and coding tasks. This architecture enables the model to efficiently process and generate accurate responses to complex queries.
 **Contact**
+Https://nextai.co.in
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nextai-team__Moe-2x7b-QA-Code)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |67.42|
+|AI2 Reasoning Challenge (25-Shot)|65.19|
+|HellaSwag (10-Shot)              |85.36|
+|MMLU (5-Shot)                    |61.71|
+|TruthfulQA (0-shot)              |65.23|
+|Winogrande (5-shot)              |77.35|
+|GSM8k (5-shot)                   |49.66|