Instructions to use streamerbtw1002/Nexuim-R1-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use streamerbtw1002/Nexuim-R1-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="streamerbtw1002/Nexuim-R1-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("streamerbtw1002/Nexuim-R1-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("streamerbtw1002/Nexuim-R1-7B-Instruct")
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 streamerbtw1002/Nexuim-R1-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "streamerbtw1002/Nexuim-R1-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "streamerbtw1002/Nexuim-R1-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/streamerbtw1002/Nexuim-R1-7B-Instruct

SGLang

How to use streamerbtw1002/Nexuim-R1-7B-Instruct 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 "streamerbtw1002/Nexuim-R1-7B-Instruct" \
    --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": "streamerbtw1002/Nexuim-R1-7B-Instruct",
		"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 "streamerbtw1002/Nexuim-R1-7B-Instruct" \
        --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": "streamerbtw1002/Nexuim-R1-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use streamerbtw1002/Nexuim-R1-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/streamerbtw1002/Nexuim-R1-7B-Instruct
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+78

-70

Files changed (1) hide show

README.md +78 -70

README.md CHANGED Viewed

@@ -1,71 +1,79 @@
----
-library_name: transformers
-tags:
-- CoT
-- Code
-license: apache-2.0
-language:
-- en
-- zh
-- ko
-- ru
-- de
-base_model: Qwen/Qwen2.5-7B-Instruct
-model_name: streamerbtw1002/Nexuim-R1-7B-Instruct
-revision: main
----
-## Model Details
-**Model Name:** streamerbtw1002/Nexuim-R1-7B-Instruct
-**Developed by:** [James Phifer](https://nexusmind.tech/) (NexusMind.tech)
-**Funded by:** [Tristian](https://shuttleai.com/) (Shuttle.ai)
-**License:** Apache-2.0
-**Finetuned from:** Qwen/Qwen2.5-VL-7B-Instruct
-**Architecture:** Transformer-based LLM
-### Overview
-This model is designed to handle complex mathematical questions efficiently using Chain of Thought (CoT) reasoning.
-- **Capabilities:**
-  - General-purpose LLM
-  - Strong performance on multi-step reasoning tasks
-  - Able to respond to requests ethically while preventing human harm
-- **Limitations:**
-  - Not evaluated extensively
-  - May generate incorrect or biased outputs in certain contexts
-## Training Details
-**Dataset:** Trained on a **120k-line** CoT dataset for mathematical reasoning.
-**Training Hardware:** 1x A100 80GB GPU (Provided by Tristian at Shuttle.ai)
-## Evaluation
-**Status:** Not formally tested yet.
-**Preliminary Results:**
-- Provides detailed, well-structured answers
-- Performs well on long-form mathematical problems
-## Usage
-```python
-from transformers import AutoConfig, AutoModel, AutoTokenizer
-model_id = "streamerbtw1002/Nexuim-R1-7B-Instruct"
-config = AutoConfig.from_pretrained(
-  model_id,
-  revision="main"
-)
-model = AutoModel.from_pretrained(
-  model_id,
-  revision="main"
-)
-tokenizer = AutoTokenizer.from_pretrained(
-  model_id,
-  revision="main"
-)
 ```

+---
+library_name: transformers
+tags:
+- CoT
+- Code
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model: Qwen/Qwen2.5-7B-Instruct
+model_name: streamerbtw1002/Nexuim-R1-7B-Instruct
+revision: main
+---
+## Model Details
+**Model Name:** streamerbtw1002/Nexuim-R1-7B-Instruct
+**Developed by:** [James Phifer](https://nexusmind.tech/) (NexusMind.tech)
+**Funded by:** [Tristian](https://shuttleai.com/) (Shuttle.ai)
+**License:** Apache-2.0
+**Finetuned from:** Qwen/Qwen2.5-VL-7B-Instruct
+**Architecture:** Transformer-based LLM
+### Overview
+This model is designed to handle complex mathematical questions efficiently using Chain of Thought (CoT) reasoning.
+- **Capabilities:**
+  - General-purpose LLM
+  - Strong performance on multi-step reasoning tasks
+  - Able to respond to requests ethically while preventing human harm
+- **Limitations:**
+  - Not evaluated extensively
+  - May generate incorrect or biased outputs in certain contexts
+## Training Details
+**Dataset:** Trained on a **120k-line** CoT dataset for mathematical reasoning.
+**Training Hardware:** 1x A100 80GB GPU (Provided by Tristian at Shuttle.ai)
+## Evaluation
+**Status:** Not formally tested yet.
+**Preliminary Results:**
+- Provides detailed, well-structured answers
+- Performs well on long-form mathematical problems
+## Usage
+```python
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+model_id = "streamerbtw1002/Nexuim-R1-7B-Instruct"
+config = AutoConfig.from_pretrained(
+  model_id,
+  revision="main"
+)
+model = AutoModel.from_pretrained(
+  model_id,
+  revision="main"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+  model_id,
+  revision="main"
+)
 ```