Instructions to use prithivMLmods/COCO-7B-Instruct-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/COCO-7B-Instruct-1M with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/COCO-7B-Instruct-1M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/COCO-7B-Instruct-1M")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/COCO-7B-Instruct-1M")
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 Settings

vLLM

How to use prithivMLmods/COCO-7B-Instruct-1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/COCO-7B-Instruct-1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/COCO-7B-Instruct-1M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/COCO-7B-Instruct-1M

SGLang

How to use prithivMLmods/COCO-7B-Instruct-1M 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 "prithivMLmods/COCO-7B-Instruct-1M" \
    --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": "prithivMLmods/COCO-7B-Instruct-1M",
		"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 "prithivMLmods/COCO-7B-Instruct-1M" \
        --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": "prithivMLmods/COCO-7B-Instruct-1M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/COCO-7B-Instruct-1M with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/COCO-7B-Instruct-1M
```

prithivMLmods commited on Jan 27, 2025

Commit

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Update README.md

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@@ -14,7 +14,7 @@ tags:
 - text-generation-inference
 - coco
 ---
-# **COCO-7B-Instruct [chain of continuesness]**
 COCO-7B-Instruct `[ chain of continuesness ]` is based on a 7B-parameter architecture, optimized for instruction-following tasks and advanced reasoning capabilities. Fine-tuned on a diverse set of datasets and leveraging chain-of-thought (CoT) reasoning, it excels in understanding contexts, solving mathematical problems, and generating detailed, structured responses. Its lightweight architecture ensures efficiency while maintaining performance, making it suitable for applications requiring logical reasoning, concise explanations, and multi-step problem-solving.
@@ -33,7 +33,7 @@ Below is a code snippet demonstrating how to load the tokenizer and model for co
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "prithivMLmods/COCO-7B-Instruct"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,

 - text-generation-inference
 - coco
 ---
+# **COCO-7B-Instruct 1M [chain of continuesness]**
 COCO-7B-Instruct `[ chain of continuesness ]` is based on a 7B-parameter architecture, optimized for instruction-following tasks and advanced reasoning capabilities. Fine-tuned on a diverse set of datasets and leveraging chain-of-thought (CoT) reasoning, it excels in understanding contexts, solving mathematical problems, and generating detailed, structured responses. Its lightweight architecture ensures efficiency while maintaining performance, making it suitable for applications requiring logical reasoning, concise explanations, and multi-step problem-solving.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/COCO-7B-Instruct-1M"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,