Instructions to use thelamapi/next-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-8b with Transformers:

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

pipe = pipeline("text-generation", model="thelamapi/next-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thelamapi/next-8b")
model = AutoModelForCausalLM.from_pretrained("thelamapi/next-8b")
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 thelamapi/next-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next-8b

SGLang

How to use thelamapi/next-8b 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 "thelamapi/next-8b" \
    --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": "thelamapi/next-8b",
		"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 "thelamapi/next-8b" \
        --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": "thelamapi/next-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thelamapi/next-8b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-8b
```

Lamapi commited on Nov 10, 2025

Commit

4256f31

verified ·

1 Parent(s): 584d209

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -2

README.md CHANGED Viewed

@@ -40,12 +40,18 @@ tags:
 - enterprise
 pipeline_tag: text-generation
 datasets:
-- mlabonne/FineTome-100k
 - CognitiveKernel/CognitiveKernel-Pro-SFT
 - OpenSPG/KAG-Thinker-training-dataset
-- Gryphe/ChatGPT-4o-Writing-Prompts
 - QuixiAI/dolphin-r1
 - uclanlp/Brief-Pro
 library_name: transformers
 ---

 - enterprise
 pipeline_tag: text-generation
 datasets:
 - CognitiveKernel/CognitiveKernel-Pro-SFT
 - OpenSPG/KAG-Thinker-training-dataset
 - QuixiAI/dolphin-r1
 - uclanlp/Brief-Pro
+- Gryphe/Opus-WritingPrompts
+- GreenerPastures/All-Your-Base-Full
+- dongguanting/ARPO-SFT-54K
+- Medint/Multi-Med-conversational
+- mlabonne/smoltalk-flat
+- mlabonne/natural_reasoning-formatted
+- QuixiAI/open-instruct-uncensored
+- mlabonne/open-perfectblend
 library_name: transformers
 ---