Instructions to use abideen/MonarchCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abideen/MonarchCoder-7B with Transformers:

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

pipe = pipeline("text-generation", model="abideen/MonarchCoder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abideen/MonarchCoder-7B")
model = AutoModelForCausalLM.from_pretrained("abideen/MonarchCoder-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abideen/MonarchCoder-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abideen/MonarchCoder-7B

SGLang

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

Docker Model Runner
How to use abideen/MonarchCoder-7B with Docker Model Runner:
```
docker model run hf.co/abideen/MonarchCoder-7B
```

abideen commited on Mar 10, 2024

Commit

6231144

verified ·

1 Parent(s): d824911

Update README.md

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@@ -135,20 +135,20 @@ MonarchCoder-7B is a slerp merge of the following models using [LazyMergekit](ht
 The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch pperforms amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-7B which performs better on OpenLLM, Nous, and HumanEval benchmark. Although [MonarchCoder-2x7B](abideen/MonarchCoder-MoE-2x7B) performs better than MonarchCoder-7B.
-# [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_abideen__MonarchCoder-7B)
-|             Metric              |Value|
-|---------------------------------|----:|
-|Avg.                             |71.17|
-|AI2 Reasoning Challenge (25-Shot)|68.52|
-|HellaSwag (10-Shot)              |87.30|
-|MMLU (5-Shot)                    |64.65|
-|TruthfulQA (0-shot)              |61.21|
-|Winogrande (5-shot)              |80.19|
-|GSM8k (5-shot)                   |65.13|
 ## 🧩 Configuration

 The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch pperforms amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-7B which performs better on OpenLLM, Nous, and HumanEval benchmark. Although [MonarchCoder-2x7B](abideen/MonarchCoder-MoE-2x7B) performs better than MonarchCoder-7B.
+## 🏆 Evaluation results
+|             Metric              |MonarchCoder-Moe-2x7B||MonarchCoder-7B||AlphaMonarch|
+|---------------------------------|---------------------|-----------------|------------|
+|Avg.                             |       74.23         |      71.17      |   75.99    |
+|HumanEval                        |       41.15         |      39.02      |   34.14    |
+|HumanEval+                       |       29.87         |      31.70      |   29.26    |
+|MBPP                             |       40.60         |       *         |     *      |
+|AI2 Reasoning Challenge (25-Shot)|       70.99         |      68.52      |   73.04    |
+|HellaSwag (10-Shot)              |       87.99         |      87.30      |   89.18    |
+|MMLU (5-Shot)                    |       65.11         |      64.65      |   64.40    |
+|TruthfulQA (0-shot)              |       71.25         |      61.21      |   77.91    |
+|Winogrande (5-shot)              |       80.66         |      80.19     .|   84.69    |
+|GSM8k (5-shot)           .       |       69.37         |      65.13      |   66.72    |
 ## 🧩 Configuration