Instructions to use AdityaPS/SpaceLLM_Multi_turn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdityaPS/SpaceLLM_Multi_turn with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b")
model = PeftModel.from_pretrained(base_model, "AdityaPS/SpaceLLM_Multi_turn")

Transformers

How to use AdityaPS/SpaceLLM_Multi_turn with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AdityaPS/SpaceLLM_Multi_turn", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdityaPS/SpaceLLM_Multi_turn with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdityaPS/SpaceLLM_Multi_turn

SGLang

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

Docker Model Runner
How to use AdityaPS/SpaceLLM_Multi_turn with Docker Model Runner:
```
docker model run hf.co/AdityaPS/SpaceLLM_Multi_turn
```

SpaceLLM Multi Turn — LoRA Adapter for Multi-Turn Conversational Chat

SpaceLLM Multi Turn is a parameter-efficient LoRA adapter fine-tuned on top of openai/gpt-oss-20b for multi-turn conversational chat. Only the attention projection layers (q_proj, k_proj, v_proj, o_proj) are trained; the full transformer backbone remains frozen, keeping the adapter extremely lightweight while steering the model's outputs toward coherent, context-aware responses across extended conversations.

Model Details

Model Description

Developed by: AdityaPS
Model type: LoRA adapter (PEFT) over a causal language model
Base model: openai/gpt-oss-20b (22B params, BF16/MXFP4)
Language(s): English
License: Apache 2.0
Task: Causal LM / multi-turn conversational chat

Adapter Configuration

Parameter	Value
PEFT type	LoRA
Rank (`r`)	16
Alpha	32
Dropout	0.05
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`
Bias	none
Task type	CAUSAL_LM
PEFT version	0.19.1

How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "openai/gpt-oss-20b"
adapter_id = "AdityaPS/SpaceLLM_Multi_turn"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)

messages = [
    {"role": "user", "content": "Hi, can you help me plan a trip?"},
    {"role": "assistant", "content": "Of course! Where are you thinking of going?"},
    {"role": "user", "content": "Somewhere in Japan, maybe Tokyo."},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

This adapter was trained using LoRA on the attention projection layers only, keeping the base model frozen. This makes the adapter lightweight to store and share while adapting the model's behavior for multi-turn, context-aware conversational chat.

Framework Versions

PEFT 0.19.1

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